Residential Proximity to Croplands at Birth and Childhood Leukaemia

Residential Proximity to Croplands at Birth and Childhood Leukaemia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Residential Proximity to Croplands at Birth and Childhood Leukaemia Sophie Bamouni, Denis Hémon, Laure Faure, Jacqueline Clavel, Stéphanie Goujon This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1323671/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Background Domestic and parental occupational pesticide exposures are suspected of involvement in the occurrence of childhood acute leukaemia (AL), but the role of exposure to agricultural activities is little known. In a previous ecological study conducted in France, we observed an increase in acute lymphoblastic leukaemia (ALL) incidence rate with increasing viticulture density in the municipalities of residence at diagnosis. Objectives this study aimed to test the hypothesis that residential proximity to croplands at birth increases the risk of childhood AL, with a particular focus on vineyards. Methods We identified all the primary AL cases diagnosed before the age of 15 years in the cohorts of children born in the French municipalities between 1990 and 2015. We estimated crop densities in each municipality of residence at birth using agricultural census data, for ten crop types. Variations in standardized incidence ratios (SIR) were evaluated with Poisson regression models, for all AL, ALL and acute myeloid leukaemia (AML), separately. Results Among the 19,809,700 children born and residing in mainland France at birth in 1990-2015, 8,747 AL cases (7,236 ALL and 1,335 AML) were diagnosed over the period. We did not evidence any statistically significant positive association between total crop density or any specific crop density in the municipality of residence at birth and all AL, ALL or AML. Interestingly, we observed a higher ALL incidence rate in the municipalities with the highest viticulture densities (SIR=1.25 95%CI [1.01-1.54]). Adjusting for the main potential confounders did not change the results. Conclusion Our study does not support the hypothesis that residential proximity to croplands, particularly vineyards, around birth plays a role in childhood leukaemia. The slightly higher ALL incidence rate in children born in the municipalities with the highest viticulture densities may reflect the previously-observed association at diagnosis. childhood leukaemia agricultural crops agricultural census pesticides prenatal exposure Figures Figure 1 Figure 2 Figure 3 1. Introduction Acute leukaemia (AL), the most frequent childhood cancer, affects around 500 children under 15 each year in France (Poulalhon et al., 2020 ). About 80% of the cases consist in acute lymphoblastic leukaemia (ALL) and 15% in acute myeloid leukaemia (AML). Domestic pesticide use and parental occupational exposure to pesticides are strongly suspected of playing a role in childhood AL. A positive association between childhood AL risk and domestic pesticide use during pregnancy and childhood has been reported in several studies and confirmed by recent meta-analyses (Bailey et al., 2015 ; Karalexi et al., 2021 ; Van Maele-Fabry et al., 2019 ). Pooled-analysis meta-analyses by the CLIC consortium have shown associations between maternal occupational exposure during pregnancy and AML risk, and between paternal occupational exposure preconception and childhood ALL (Bailey et al., 2014 ). Proximity to cropland and agricultural pesticide exposure have been subject to less investigation. Several case-control studies have exploited land-use data (Malagoli et al., 2016 ; Nguyen et al., 2021 ), satellite images (Gómez-Barroso et al., 2016 ) or aerial photographs (Carozza et al., 2009 ) to estimate the agricultural area in the vicinity of the participants’ geocoded residential addresses, while others, with an ecological design, considered agricultural census data to evaluate the agricultural activity in the area of residence (county or municipality level) (Booth et al., 2015 ; Carozza et al., 2008 ; Coste et al., 2020 ; Walker et al., 2007 ). Some studies reported positive associations with particular crop types, with, however, no consistency (Booth et al., 2015 ; Carozza et al., 2008 ; Gómez-Barroso et al., 2016 ; Nguyen et al., 2021 ). In California, where agricultural pesticide applications are systematically registered with information on the type of pesticides used, quantity applied, crop type and treated area, some studies (Park et al., 2020 ; Reynolds et al., 2005b , 2002 ; Rull et al., 2009 ) found a greater AL risk with several chemical types of pesticide, while in another study no association was observed (Reynolds et al., 2005a ). In Denmark, a cohort study estimated agricultural pesticide exposure around the geocoded addresses of residence during pregnancy based on pesticide sales data and did not identify any association with childhood AL risk (Patel et al., 2020 ). In a recent ecological study, we observed a log-linear increase in ALL incidence rate with increasing viticulture density in the municipalities of residence at the time of diagnosis, with a 16% increase in the municipalities in which more than 25% of their area was devoted to viticulture (Coste et al., 2020 ). In this study, we investigated whether the previously-observed association with viticulture near the residence at the time of diagnosis might reflect a stronger relationship with prenatal exposure. More generally, we tested the hypothesis that the proximity of home to crops and agricultural activities during the prenatal period, a critical period for leukaemogenesis, increased the risk of childhood AL. The study was conducted on the population of children born between 1990 and 2015 in mainland France and took the place of residence at birth as a proxy for residence during the prenatal period. 2. Population And Methods 2.1. Population The population under study consisted in children under 15 born in mainland France between 1990 and 2015. The 19,809,700 births (761,873 per year on average) domiciled in the municipalities of mainland France over the study period were provided by the French National Institute for Statistics and Economic Studies (INSEE). The at-risk populations were estimated assuming that death and migration rates were negligible. All the primary AL cases in that population were identified in the French national registry of childhood cancer (RNCE). Of the 9,293 AL cases born and diagnosed in 1990-2015, 269 cases born abroad were excluded. We obtained legal authorisation to access the birth certificates from the town halls of the municipalities of birth (i.e. the municipalities where the maternity hospitals were located) that were recorded in the RNCE. Birth certificates stating the address of the mothers' residences were obtained for 97% of the remaining cases (277 missing addresses). A total of 8,747 AL cases aged under 15 were identified among the children born in 1990-2015 and domiciled at birth in one of the 35,511 municipalities of mainland France. There were 7,236 ALL (6,970 B-cell precursor ALL, BCP-ALL), and 1,335 AML cases. 2.2. Crop density The crop-growing areas of the municipalities were estimated for the birth periods 1990-1994, 1995-2004, and 2005-2015 using data from the 1988, 2000, and 2010 agricultural censuses, respectively (Ministère de l’agriculture et de l’alimentation, 2012 ). Crop density was defined as the ratio of the crop area to the total municipality area (about 15 km 2 on average) for each permanent, i.e. viticulture and arboriculture, and non-permanent crop, i.e. straw cereals, maize, rapeseed, sunflowers, fresh vegetables, dry vegetables and protein crops, potatoes, and beet. The methodology was the same as that of the ecological study at the time of diagnosis (Coste et al., 2020 ). Briefly, total crop density was considered as a 5-category variable in which the first category was constituted by municipalities with a total crop density of less than 5%, and the others by the population-weighted quartiles of the total crop density. Crop-specific qualitative variables were similar, except for the first category, which was split into a reference category consisting of municipalities with a total crop density of less than 5%, and a category with at least 5% of total crops but less than 5% of the specific crop. For additional analyses on viticulture densities, we split the highest density categories into two semi-quartiles. 2.3. Statistical analysis We used Poisson regression models to test the association between municipality crop densities and AL incidence rates. The number of AL cases expected over the study period was estimated for each birth period (1990-1994, 1995-2004, 2005-2015) and each municipality of residence at birth using national age-specific incidence rates as reference rates. We checked that the annual 0-14-year and age-specific incidence rates were homogeneous over the period 1990-2015 (Additional. Figure 1 ). The analyses were conducted for all AL taken together, and separately for ALL (particularly BCP-ALL) and AML. The standardized incidence ratios (SIR) and their 95% confidence intervals were estimated by Poisson regression models. Likelihood ratios were used to test for heterogeneity of SIRs. SIR variations (SIRR) for a 10% increase in crop density were also estimated. The log-linearity hypothesis was tested by likelihood ratio tests between the qualitative model and a semi-quantitative model assigning the population-weighted average value of the crop density to all municipalities in the category. The size of urban unit (Paris urban unit vs . other urban units) was systematically included in the exposure-response models for AL, ALL and BCP-ALL in order to improve model fit. The statistical significance level was set at 5% for all the tests and the p-values for exposure-response models were estimated using one-sided tests. Sensitivity analysis The size of the urban unit (Paris urban unit vs . other urban units), residential UV radiation exposure (UV >105.5 J/cm 2 vs . UV ≤ 105.5 J/cm 2 ) and French deprivation index FDep (Rey et al., 2009 ) (the most deprived quintile Q5 vs. other quintiles Q1-Q4), which were associated with childhood ALL in our previous studies (Marquant et al., 2016 ; Coste et al., 2017 ), were considered as potential confounders and adjusted for in the sensitivity analyses conducted on ALL and BCP-ALL. Additional analyses were performed after excluding the Paris urban unit or urban units with a population greater than 100,000, and by stratifying the analyses by age group (0-6 and 7-14 years old). For viticulture, we also performed an analysis for ALL restricted to the 1990-2004 birth period in order to account for a vine uprooting policy introduced in 2007. All the analyses were performed using R 3.6.3 software ( https://cran.r-project.org/ ). 3. Results One third of the births were domiciled in 5.7% of the municipalities with a total crop density lower than 5%. The majority of municipalities had at least 50% cropland (figure 1 ). The distribution of total crop and specific crop densities weighted by the number of births in the municipalities are shown in Table 1 . The municipalities with at least 19% total crop area accounted for 50% of births over the study period. The crop specific distributions were asymmetric and varied depending on the type of crop. In particular, for viticulture, very few children (1%) lived at the time of birth in municipalities with a high viticulture density (p99=33.1%). The spatial distribution of specific crop densities is shown in figure 2 . Intensive viticulture was mainly practiced in the South-West and Mediterranean regions. Arboriculture is quite rare in France. Straw cereals are the most common type of crop and predominant in the North of France. Table 1 Distribution 1 of the total crop and specific crop densities in the French municipalities Crop P5 P25 P50 P75 P95 P99 All crops 0 1.5 19.0 48.1 73.5 115.7 2 Permanent crops Viticulture 0 0 0 0.03 1.5 33.1 Arboriculture 0 0 0 0.1 0.9 11.0 Non-permanent crops Straw cereals 0 0 2.6 11.8 24.3 55.8 Fresh vegetables 0 0 0.02 0.3 1.5 9.2 Maize 0 0 0 2.1 7.6 21.8 Rapeseed 0 0 0 0.8 3.4 13.1 Sunflower 0 0 0 0 1.7 11.9 Potatoes 0 0 0 0.04 0.4 7.9 Dry vegetables 0 0 0 0.2 1.7 8.8 Beet 0 0 0 0 1.6 13.8 1 Distribution weighted by numbers of births in the municipalities, over 3 birth periods (based on the 1988, 2000 and 2010 agricultural censuses). P5, P25, P50, P75, P95, P99: 5th, 25th, 50th, 75th, 95th and 99th population weighted percentiles of the crop densities (%), respectively. 2 99th percentile is greater than 100% because the cropland areas reported in the agricultural censuses are assigned to the municipality of the farm headquarter, which may not be the municipality where the land plots are situated. 3.1. Association between crop densities and childhood AL incidence We did not evidence any association between total crop density in the municipality of residence at birth and all AL, ALL and AML (Table 2 ). Table 2 Association between childhood leukaemia incidence rate and crop densities a (all crops and permanent crops) AL ALL AML O E SIR 95% CI p b O E SIR 95% CI p b O E SIR 95% CI p b Total crops Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.54 2285 2308.5 0.99 0.95-1.03 0.67 422 440.7 0.96 0.87-1.05 0.26 [5-18.7[ 1476 1485.4 0.99 0.94-1.05 1211 1228.4 0.99 0.93-1.04 231 224.1 1.03 0.91-1.17 [18.7-37.4[ 1507 1485.1 1.01 0.96-1.07 1263 1228.2 1.03 0.97-1.09 212 223.3 0.95 0.83-1.09 [37.4-61.0[ 1541 1490.7 1.03 0.98-1.09 1259 1232.8 1.02 0.97-1.08 252 223.3 1.13 1.00-1.28 ≥61 1465 1497.1 0.98 0.93-1.03 1218 1238.1 0.98 0.93-1.04 218 223.6 0.98 0.85-1.11 SIRR for ∆x= 10% c 1.00 0.99-1.01 0.47 1238.1 1.00 0.99-1.01 0.44 223.6 1.00 0.99-1.01 0.31 Viticulture Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.84 2285 2308.5 0.99 0.95-1.03 0.88 422 440.7 0.96 0.87-1.05 0.62 Total crop density ≥5% and viticulture <5% 5413 5391.7 1.00 0.98-1.03 4465 4459.2 1.00 0.97-1.03 834 809.7 1.03 0.96-1.10 [5-10.0[ 143 141.2 1.01 0.86-1.19 124 116.8 1.06 0.89-1.27 17 21.1 0.81 0.50-1.30 [10.0-16.2[ 141 143.7 0.98 0.83-1.16 116 118.8 0.98 0.81-1.17 22 21.5 1.02 0.67-1.56 [16.2-24.7[ 138 142.0 0.97 0.82-1.15 120 117.5 1.02 0.85-1.22 17 21.2 0.80 0.50-1.29 ≥24.7 154 139.5 1.10 0.94-1.29 126 115.4 1.09 0.92-1.30 23 20.8 1.10 0.73-1.66 SIRR for ∆x= 10% c 1.01 0.98-1.04 0.24 115.4 1.01 0.98-1.05 0.23 20.8 1.01 0.93-1.09 0.45 Arboriculture Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.07 2285 2308.5 0.99 0.95-1.03 0.15 422 440.7 0.96 0.87-1.05 0.33 Total crop density ≥5% and arboriculture <5% 5809 5742.1 1.01 0.99-1.04 4799 4748.8 1.01 0.98-1.04 887 861.8 1.03 0.96-1.10 [5-6.4[ 54 54.1 1.00 0.76-1.30 45 44.7 1.01 0.75-1.35 9 8.2 1.10 0.57-2.12 [6.4-9.34[ 38 53.3 0.71 0.52-0.98 30 44.1 0.68 0.48-0.97 7 8.1 0.87 0.41-1.82 [9.4-13.1[ 42 51.1 0.82 0.61-1.11 39 42.2 0.92 0.67-1.26 3 7.6 0.39 0.13-1.22 ≥13.1 46 57.6 0.80 0.60-1.07 38 47.6 0.80 0.58-1.10 7 8.6 0.81 0.39-1.70 SIRR for ∆x= 10% c 0.87 0.79-0.97 0.99 47.6 0.89 0.79-0.99 0.98 8.6 0.78 0.58-1.06 0.94 AL: Acute leukaemia; ALL: Acute lymphoblastic leukaemia; AML: Acute myeloid leukaemia; O: Observed number of cases; E: Expected number of cases; SIR: Standardized Incidence Ratio; 95% CI: 95% Confidence Interval; a The total crop density and the specific crop density in a municipality were defined as the ratio of the total area used for agriculture and the area used for the specific crop, respectively, over the total area of the municipality (based on national agricultural census data). b p-value of the tests (chi-square test of heterogeneity of categories of crop density and test for the regression coefficient in the log-linear Poisson regression model, H0: β ≤ 0 vs H1: β > 0) c SIRR (for Δ x = 10%) = Relative Standardized Incidence Ratio: multiplicative variation in the SIR for a 10% increase in the crop density derived from a log-linear Poisson regression model, with adjustment on the size of urban unit (Paris vs other urban units) for AL and ALL. The p values for the tests for departure from log-linearity ranged from 0.31 to 0.99. For viticulture, no statistically significant heterogeneity of the SIRs for the density categories was observed for AL or its subgroups; there was no evidence of a log-linear association (Table 2 ). The SIR was slightly higher in the municipalities with the highest viticulture densities (1.10 95%CI [0.94-1.29] for AL) while closer to 1 for the other categories. Although far from being statistically significant, the slight increase for the highest category resembles that observed in our ecological study at the time of diagnosis. We subdivided the viticulture density quartiles into semi-quartiles. There was no change in the log-linear trend parameter estimates, but a greater SIR in the highest density category (municipalities with a viticulture density greater than 37.7%) was detected for AL and ALL (SIR=1.25 95%CI [1.01-1.54] and SIR=1.23 95%CI [0.97-1.55], respectively, figure 3 ). The arboriculture density analyses did not show any positive association with AL or its subgroups (Table 2 ). We did not observe any heterogeneity of the SIRs for the density categories of non-permanent crops, or any positive log-linear association with AL or its subgroups, ALL and AML (Table 3 ). Table 3 Association between the incidence rate of childhood leukaemia and non-permanent crop densities a AL ALL AML O E SIR 95% CI p b O E SIR 95% CI p b O E SIR 95% CI p b Straw cereals Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.87 2285 2308.5 0.99 0.95-1.03 0.69 422 440.7 0.96 0.87-1.05 0.84 Total crop density ≥5% and straw cereals <5% 2271 2290.1 0.99 0.95-1.03 1868 1893.8 0.99 0.94-1.03 346 343.5 1.01 0.91-1.12 [5.0-8.8[ 920 912.7 1.01 0.94-1.08 759 754.9 1.01 0.94-1.08 143 137.1 1.04 0.89-1.23 [8.8-14.2[ 944 910.5 1.04 0.97-1.11 796 753.1 1.06 0.99-1.13 132 136.7 0.97 0.81-1.14 [14.2-23.8[ 920 919.4 1.00 0.94-1.07 758 760.4 1.00 0.93-1.07 146 138.2 1.06 0.90-1.24 ≥23.8 934 925.4 1.01 0.95-1.08 770 765.3 1.01 0.94-1.08 146 138.7 1.05 0.89-1.24 SIRR for ∆x= 10% c 1.00 0.99-1.02 0.36 765.3 1.00 0.98-1.02 0.42 138.7 1.02 0.98-1.06 0.14 Maize Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.39 2285 2308.5 0.99 0.95-1.03 0.73 422 440.7 0.96 0.87-1.05 0.25 Total crop density ≥5% and maize <5% 4666 4612.2 1.01 0.98-1.04 3850 3815.0 1.01 0.98-1.04 716 693.3 1.03 0.96-1.11 [5-7.0[ 314 332.9 0.94 0.84-1.05 273 275.2 0.99 0.88-1.12 37 49.8 0.74 0.54-1.03 [7.03-9.8[ 345 332.4 1.04 0.93-1.15 286 274.8 1.04 0.93-1.17 53 49.6 1.07 0.82-1.40 [9.83-14.4[ 347 335.8 1.03 0.93-1.15 279 277.6 1.01 0.89-1.13 58 50.1 1.16 0.89-1.50 ≥14.4 317 344.9 0.92 0.82-1.03 263 285.0 0.92 0.82-1.04 49 51.5 0.95 0.72-1.26 SIRR for ∆x= 10% c 1.00 0.95-1.04 0.59 285.0 0.99 0.95-1.04 0.60 51.50 1.04 0.93-1.15 0.25 Rapeseed Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.79 2285 2308.5 0.99 0.95-1.03 0.65 422 440.7 0.96 0.87-1.05 0.47 Total crop density ≥5% and rapeseed <5% 5392 5388.0 1.00 0.97-1.03 4450 4456.1 1.00 0.97-1.03 828 808.6 1.02 0.96-1.10 [5-6.1[ 153 140.3 1.09 0.93-1.28 129 116.0 1.11 0.94-1.32 24 21.2 1.13 0.76-1.69 [6.1-7.9[ 152 140.8 1.08 0.92-1.27 130 116.4 1.12 0.94-1.33 18 21.2 0.85 0.54-1.35 [7.9-11.0[ 141 141.4 1.00 0.85-1.18 121 116.9 1.03 0.87-1.24 16 21.2 0.75 0.46-1.23 ≥11.0 151 147.7 1.02 0.87-1.20 121 122.1 0.99 0.83-1.18 27 22.1 1.22 0.84-1.78 SIRR for ∆x= 10% c 1.02 0.95-1.10 0.30 122.1 1.03 0.95-1.11 0.26 22.10 1.01 0.83-1.22 0.46 Sunflowers Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.62 2285 2308.5 0.99 0.95-1.03 0.72 422 440.7 0.96 0.87-1.05 0.48 Total crop density ≥5% and sunflowers <5% 5634 5600.6 1.01 0.98-1.03 4656 4631.8 1.01 0.98-1.03 853 840.9 1.01 0.95-1.08 [5-6.2[ 75 88.9 0.84 0.67-1.06 64 73.6 0.87 0.68-1.11 11 13.3 0.83 0.46-1.50 [6.2-8.2[ 91 88.9 1.02 0.83-1.26 71 73.6 0.97 0.76-1.22 20 13.3 1.51 0.97-2.33 [8.2-11.9[ 92 89.9 1.02 0.83-1.26 77 74.4 1.04 0.83-1.29 15 13.4 1.12 0.67-1.85 ≥11.9 97 89.8 1.08 0.89-1.32 83 74.3 1.12 0.90-1.39 14 13.4 1.04 0.62-1.76 SIRR for ∆x= 10% 1.00 0.91-1.09 0.53 74.3 1.01 0.92-1.11 0.43 13.41 1.06 0.86-1.32 0.28 Potatoes Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.73 2285 2308.5 0.99 0.95-1.03 0.68 422 440.7 0.96 0.87-1.05 0.42 Total crop density ≥5% and potatoes <5% 5803 5792.4 1.00 0.98-1.03 4798 4790.5 1.00 0.97-1.03 885 869.6 1.02 0.95-1.09 [5-6.3[ 46 41.1 1.12 0.84-1.49 42 34.0 1.24 0.91-1.67 4 6.1 0.65 0.24-1.74 [6.3-8.5[ 47 41.7 1.13 0.85-1.50 34 34.5 0.99 0.70-1.38 11 6.2 1.77 0.98-3.19 [8.47-12.6[ 48 41.6 1.15 0.87-1.53 40 34.4 1.16 0.85-1.58 7 6.2 1.13 0.54-2.36 ≥12.6 45 41.3 1.09 0.81-1.46 37 34.2 1.08 0.78-1.50 6 6.2 0.97 0.44-2.17 SIRR for ∆x= 10% c 1.06 0.95-1.20 0.15 34.2 1.04 0.91-1.18 0.30 6.16 1.13 0.85-1.51 0.20 Fresh vegetables Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.10 2285 2308.5 0.99 0.95-1.03 0.34 422 440.7 0.96 0.87-1.05 0.50 Total crop density ≥5% and fresh vegetables <5% 5758 5729.6 1.00 0.98-1.03 4763 4738.5 1.01 0.98-1.03 877 859.9 1.02 0.95-1.09 [5-6.11[ 62 56.8 1.09 0.85-1.40 52 47.0 1.11 0.84-1.45 9 8.6 1.04 0.54-2.00 [6.11-7.7[ 40 57.1 0.70 0.51-0.95 35 47.3 0.74 0.53-1.03 5 8.5 0.59 0.24-1.41 [7.7-11.5[ 58 56.4 1.03 0.80-1.33 45 46.6 0.97 0.72-1.29 12 8.5 1.42 0.81-2.50 ≥11.5 71 58.3 1.22 0.97-1.54 56 48.2 1.16 0.89-1.51 10 8.8 1.14 0.61-2.12 SIRR for ∆x= 10% c 1.07 0.98-1.18 0.05 48.2 1.04 0.94-1.16 0.20 8.79 1.18 0.96-1.44 0.05 Dry vegetables Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.75 2285 2308.5 0.99 0.95-1.03 0.64 422 440.7 0.96 0.87-1.05 0.58 Total crop density ≥5% and dry vegetables <5% 5725 5687.1 1.01 0.98-1.03 4740 4703.3 1.01 0.98-1.04 864 853.7 1.01 0.95-1.08 [5-5.9[ 63 67.7 0.93 0.73-1.19 47 56.0 0.84 0.63-1.12 15 10.1 1.48 0.89-2.46 [5.9-7.2[ 59 67.7 0.87 0.68-1.13 48 56.0 0.86 0.65-1.14 11 10.1 1.08 0.60-1.96 [7.23-9.8[ 68 68.0 1.00 0.79-1.27 57 56.3 1.01 0.78-1.31 10 10.2 0.98 0.53-1.82 ≥9.8 74 67.7 1.09 0.87-1.37 59 56.0 1.05 0.82-1.36 13 10.1 1.29 0.75-2.21 SIRR for ∆x= 10% c 0.95 0.84-1.06 0.83 56.0 0.89 0.78-1.02 0.95 10.11 1.14 0.96-1.36 0.07 Beet Total crop density <5% 2758 2788.8 0.99 0.95-1.03 0.60 2285 2308.5 0.99 0.95-1.03 0.76 422 440.7 0.96 0.87-1.05 0.49 Total crop density ≥5% and beets <5% 5601 5545.5 1.01 0.98-1.04 4631 4586.2 1.01 0.98-1.04 854 832.3 1.03 0.96-1.10 [5-6.9[ 91 103.9 0.88 0.71-1.08 75 85.9 0.87 0.70-1.09 12 15.6 0.77 0.44-1.36 [6.9-9.0[ 95 103.4 0.92 0.75-1.12 82 85.5 0.96 0.77-1.19 12 15.6 0.77 0.44-1.36 [9.04-13.0[ 105 102.7 1.02 0.84-1.24 83 84.9 0.98 0.79-1.21 20 15.4 1.30 0.84-2.02 ≥13 97 102.6 0.95 0.77-1.15 80 84.9 0.94 0.76-1.17 15 15.5 0.97 0.59-1.61 SIRR for ∆x= 10% c 0.95 0.87-1.03 0.90 84.9 0.94 0.86-1.03 0.90 15.45 1.01 0.83-1.23 0.47 AL: Acute leukaemia; ALL: Acute lymphoblastic leukaemia; AML: Acute myeloid leukaemia; O: Observed number of cases; E: Expected number of cases; SIR: Standardized Incidence Ratio; 95% CI: 95% Confidence Interval; a The crop density is defined as the ratio of the area used for the crop over the total area of the municipality (based on national agricultural census data). b p-value of the tests (chi-square test of heterogeneity of SIRs of agricultural density categories, test for the regression coefficient in the log-linear Poisson regression model, H0:β ≤ 0 vs H1: β > 0) c SIRR (for Δ x = 10%)= Relative Standardized Incidence Ratio: multiplicative variation in the SIR for a 10% increase in the crop density derived from of a log-linear Poisson regression model, with adjustment on the size of urban unit (Paris vs other urban units) for AL and ALL. The p-values for the tests for departure from log-linearity ranged from 0.08 to 0.92. 3.2. Sensitivity and additional analyses We examined the associations between ALL incidence rate and the potential ecological confounders by fitting a Poisson regression model with each variable separately (Additional. Table 1 ). The size of the urban unit was associated with ALL incidence rate, with an 11% decrease in ALL incidence rate in the population of children living in the Paris urban unit at birth, compared to other urban units (SIRR=0.89 95%CI [0.84-0.95]). ALL incidence rate also tended to be lower for children living in the most deprived municipalities, although statistical significance was not attained (SIRR=0.96 95%CI [0.91-1.02]). As expected from our previous studies, residential UV radiation was positively associated with ALL (SIRR=1.06 95%CI [1.01-1.12]). These factors were also associated with crop densities (Additional. Table 2 ). A large majority (83%) of children who lived in the Paris urban unit at birth was located in municipalities with less than 5% cropland, compared to 20% for the other urban units. For almost all crops, less than 0.5% of children in the Paris urban unit lived in municipalities in the highest crop density category. The categories with viticulture density ≥ 5% were more frequent in the municipalities with UV radiation >105.5 J/cm 2 , with around 4% of the at-risk population in each category vs <1% in municipalities with UV radiation ≤ 105.5 J/cm 2 . The same association was observed for arboriculture and sunflower densities. In contrast, UV radiation level tended to be negatively associated with rapeseed, dry vegetable and beet densities. The most deprived municipalities had a lower proportion of at-risk population in the first total crop density category. For specific crop densities, the distribution of the at-risk population in the last 4 categories was very similar in the most deprived municipalities and in other municipalities, except for straw cereals. In sensitivity analyses on AL and ALL, we adjusted for residential UV radiation exposure and FDep 2006 index of the municipalities in addition to the size of urban unit already included in the regression models. The results did not differ from those of the main analyses (Additional. Table 3 ). Analyses performed by excluding the Paris urban unit, or municipalities from the most populated urban units (population > 100,000) did not change the results (Additional. Table 4). When we stratified the analyses by two age groups (0-6 and 7-14 years old), we found a significant positive log-linear association between rapeseed density and ALL incidence among 7-14-year olds (SIRR=1.29 95%CI [1.12-1.49] per 10% increase in rapeseed density, Additional. Table 5). The numbers of cases in the municipalities with the highest rapeseed densities were, however, quite limited (25-30 expected cases) and the SIRs were heterogeneous, with values greater than one in the third and last categories (Additional. Figure 2 ). For the other crops, the results remained stable by age-group (Additional. Table 5). The analyses with viticulture restricted to children born during the period 1990-2004 resulted in the same conclusion (data not shown). 4. Discussion We investigated the association between total and specific-crop densities in the municipalities of residence at birth and childhood leukaemia incidence rates in the population of children born in 1990-2015. In all, we did not observe any statistically significant positive association between total childhood AL, ALL and AML incidence rates and any crop type. Interestingly, the incidence rate of AL was slightly higher in children living at birth in the municipalities with the highest viticulture densities (SIR=1.25 95%CI [1.01-1.54] in the highest semi-quartile of exposure), which was in line with our study on residences at diagnosis (Coste et al., 2020 ). Based on the same methodology and same agricultural data, we evidenced a log-linear increase in ALL incidence rate over the period 1990-2014 with increasing viticulture density in the municipality of residence at diagnosis (SIRR=1.04 95%CI [1.00-1.06] per 10% increase) and an increase in ALL incidence rate in the municipalities with the highest viticulture density (i.e. with more than a quarter of their area used for viticulture, SIR=1.17 95%CI [1.01-1.35]) (Coste et al., 2020 ). Although the result of the present study is not statistically significant, and less marked than in our previous study, it provides some support for the hypothesis that there is a positive association between childhood AL incidence and viticulture density in the municipalities. However, the weakness of the association does not suggest that our previous results can be explained by prenatal exposures. Viticulture is all the more of interest in that vines are a permanent crop subject to many pesticide treatments, particularly fungicidal treatments. Few studies have investigated the role of residential exposure to agricultural pesticides in the occurrence of childhood AL using proximity to croplands as a surrogate (Additional. Table 6). Most of the studies were conducted in the USA. With a large number of cases (6,168 AL), Carozza et al. (Carozza et al., 2008 ) considered the percentage cropland devoted to farming at the county level and showed a positive association between total cropland (≥60% of county total acreage vs < 20%) and ALL and AML risk (OR=1.3 95%CI [1.1-1.4], and OR=1.8 95% CI [1.4-2.3], respectively). The authors also reported positive associations with some specific crops (maize and soya bean densities for AML, oats density for ALL). In a study by Booth et al. (Booth et al., 2015 ) conducted in 6 Midwestern states, 0-4-year AL and ALL, incidence rates were associated with dry beans and sugar beet in exposure-response analyses. A positive association between 0-4-year AML incidence rates and oats density was also reported. More recently, a study conducted in California reported a greater ALL risk in children residing close to plant nurseries at birth (OR for <75 m vs ≥ 600 m of 3.09 95% CI [1.14-8.34] (Nguyen et al., 2021 )). Conversely, three Texan studies (Carozza et al., 2009 ; Thompson et al., 2008 ; Walker et al., 2007 ) did not evidence any association between childhood AL and agricultural area in the county of residence at birth (Thompson et al., 2008 ; Walker et al., 2007 ) or within a 1000-m buffer around the geocoded address of residence at birth (Carozza et al., 2009 ). In addition to our previous ecological study (Coste et al., 2020 ), three studies have been conducted in Europe (Gómez-Barroso et al., 2016 ; Malagoli et al., 2016 ; Patel et al., 2020 ). In Italy, in a case-control study with 111 AL cases and 444 matched controls, Malagoli et al. did not find any association between AL risk and arable crop, orchard, vineyard or vegetable densities (Malagoli et al., 2016 ), while, in Spain, Gomez-Barroso et al. reported an elevated risk of leukaemia (1,062 cases) with total crop density and several specific crop densities (arable land or permanently irrigated land; rice fields; heterogeneous agricultural areas) (Gómez-Barroso et al., 2016 ). The study also found strong associations with other childhood cancer types; it is unclear whether those results may be due to different addresses being used for the cases (at diagnosis) and controls (at birth). A recent Danish cohort study (Patel et al., 2020 ) found a 2-fold increase in leukaemia risk (61 AL cases) for children whose mothers lived close to agricultural areas during pregnancy (≥ 24 ha of total agricultural land within a 500-m buffer), in particular when grass/clover, peas and maize crops were present. Several studies thus reported positive associations between childhood AL risk and proximity to cropland, particularly around diagnosis (Booth et al., 2015 ; Carozza et al., 2008 ; Coste et al., 2020 ; Gómez-Barroso et al., 2016 ; Malagoli et al., 2016 ; Patel et al., 2020 ). However, the estimates of the reported associations were sometimes imprecise due to the limited numbers of cases (Gómez-Barroso et al., 2016 ; Malagoli et al., 2016 ; Patel et al., 2020 ). In addition, the positive associations reported in several studies were for total crop density (Carozza et al., 2008 ; Gómez-Barroso et al., 2016 ; Patel et al., 2020 ), which may not be a relevant surrogate for pesticide exposure because of the heterogeneity of the crops included, especially in terms of pesticide use. It is difficult to compare our results with those from other countries because of differences in agricultural areas and practices (FAO, 2019 ). For example, in the USA, positive associations were reported with dry beans, sugar beet (Booth et al., 2015 ), oats (Booth et al., 2015 ; Carozza et al., 2008 ), maize and soya beans (Carozza et al., 2008 ), which are less common crops in France (FAO, 2019 ). We used agricultural census data to estimate crop densities in the municipalities of residence at birth, as indicators of potential exposure to agricultural pesticides. A limitation consists in the fact that the census locates the crops in the municipality of the farm headquarters, while the crops may be located in neighbouring municipalities. However, the resulting misclassifications most likely affect similar agricultural areas close to each other and preserve contrasts between exposed and unexposed municipalities. Another limitation is that we assigned the agricultural census closest to the year of birth, which may have induced misclassification for rotational crops when the years of census and birth differed. This limitation should not affect the classification of permanent crops like vineyards and arboriculture. Moreover, the results remained unchanged in the sensitivity analyses restricted to children born in 1990-2004, before many vines were uprooted. The agricultural censuses do not distinguish organically-farmed fields from conventionally-farmed fields. However, organically-farmed fields constitute only a small fraction of the agricultural area of France; they accounted for 9.5% of the total agricultural area in 2019 (Ministère de agriculture et de l’alimentation, 2021) and probably less during our study period since organic farming was less widespread. Moreover, two surveys of agricultural practices showed that more than 90% of the areas covered by the crops we considered in our study had received at least one pesticide treatment in 2006 and 2011 (Ministère de l’agriculture et de l’alimentation, 2014 , 2010 ). Adjustments for the degree of urbanization, deprivation and UV radiation, which were associated with childhood ALL incidence rate at the municipality level in our previous studies, did not change the result. We were unable to take individual factors like domestic pesticide use and parental agricultural occupation into account. Even if those factors are more prevalent in the most agricultural municipalities, the proportion of children with a parent occupationally exposed to pesticides is very low in France (Bailey et al., 2014 ). Maternal use of pesticides during pregnancy is much more common, about 35% for control mothers in two French case-control studies (Mavoungou et al., 2020; Rios et al., 2017 ; Rudant et al., 2007; Vidart d’Egurbide Bagazgoïtia et al., 2018) but not restricted to agricultural areas. Residential exposure to traffic-related air pollution, which was associated with the risk of childhood AML (Amigou et al., 2011 ; Houot et al., 2015 ), could not be accounted for. However, the results were unchanged after exclusion of the municipalities in urban units with a population greater than 100,000, where those associations were observed. Our study has several strengths. A major asset is that our findings are based on a large number of cases, identified from a population-based registry. The high degree of completeness, avoided selection biases, and the high standard of diagnosis classifications and high reliability of addresses (3% missing addresses) minimised misclassifications. Another asset is the use of agricultural census data, collected on an exhaustive basis and providing the detailed distribution of ten types of crops on the fine scale of the municipality for the entire country. The crop types are known to be quite different in terms of average annual number of pesticide treatments, percentage of area treated, and main target pesticide, with variations between time periods and regions (Ministère de l’agriculture et de l’alimentation, 2014 , 2010 ). A few Californian studies benefitted from the Pesticides Use Reporting (PUR) system, with a large database on pesticides applied to crops in the state of California, to investigate for associations with childhood leukaemia (Park et al., 2020 ; Reynolds et al., 2005b , 2005a , 2002 ; Rull et al., 2009 ). Some positive associations with specific substances or classes of pesticide have been reported, with, however, heterogeneous results: AL risk was thus associated with high use of propargite near the residence at diagnosis in Reynold et al. 2002 (Reynolds et al., 2002 ); a moderate lifetime averaged use of insecticides and fumigants in the vicinity of the addresses of residence in Rull et al. (Rull et al., 2009 ); the use of metam sodium and dicofol near the residence at birth for children aged 0-4 years in Reynold et al. 2005 (Reynolds et al., 2005b ); and the uses of any carcinogenic pesticide, several chemical classes or individual pesticides near the residence at birth for children aged less than 6 years in Park et al. (Park et al., 2020 ). Our next step will be to conduct a large case-control study using geocoded addresses and a geographical information system in order to evaluate, as precisely as possible, the presence of cropland in the vicinity of the residential addresses, with a particular focus on viticulture. Elucidating the relationship between the ecological and individual crop proximity indicators will be of great importance. Future challenges will then consist in enhanced assessment of the role of agricultural pesticide exposure and identification of the specific substances potentially involved in childhood leukaemia. 5. Conclusion The present study did not evidence statistically significant associations between the density of crops in the municipality of birth and the incidence rates of childhood AL. The slightly higher ALL incidence rate in children born in the municipalities with the highest viticulture densities may reflect the association that we previously observed at diagnosis. But, overall, our results do not support the hypothesis that prenatal exposure to neighbouring agricultural activity, particularly viticulture, plays a role in childhood leukaemia. Declarations Ethics approval and consent to participate The research undertaken with the RNCE data is covered by agreements on the ethical use of data and the protection of personal data, and have been approved by French national authorities (French data protection authority CNIL No. 998198). Consent for publication Not applicable Availability of data and materials Data and programs supporting the results of the study have been archived by the Inserm EPICEA team, in compliance with the general data protection regulation. Code Availability R software code supporting the results of the study have been archived by the Inserm EPICEA team, in compliance with the general data protection regulation Competing interests The authors declare that they have no competing interests. Funding The project was supported by the French National Cancer Institute (INCa); the French National Institute of Health and Medical Research (INSERM); the French National Research Program for Environmental and Occupational Health of the Anses with the support of the Cancer TMOI of the French National Alliance for Life and Health Sciences (AVIESAN) (EST-2016/1/161) and Santé Publique France. Sophie Bamouni also received funding from Fondation de France (grant number 00089810) for her PhD. Authors’ contributions SB : Methodology, Formal analysis, Writing-Original draft preparation, Review and Editing; DH : Conceptualization, Methodology, Supervision, Writing-Review and Editing; LF : Data Curation, Writing-Review and Editing; JC : Conceptualization, Methodology, Supervision, Writing-Review and Editing, Project administration; SG : Conceptualization, Methodology, Supervision, Writing-Review and Editing Acknowledgments The authors are grateful to the staff of the French National Registry of Childhood Cancer (RNCE), who collected the incidence data. The authors also thank the paediatric oncology teams for their help in data collection. The authors thank ADISP (Archives de Données Issues de la Statistique Publique) for providing birth data from the National Institute of Statistics and Economic Studies INSEE (application form No. 19728). Authors’informations Authors list: Sophie Bamouni 1,2 (0000-0002-7267-1589), Denis Hémon 1,2 , Laure Faure 1,2,3 , Jacqueline Clavel 1,2,3 ( 0000-0002-3616-7676), Stéphanie Goujon 1,2,3 (0000-0002-4149-2246). 1 Inserm, UMR 1153 Center of Research in Epidemiology and StatisticS (CRESS), Epidemiology of childhood and adolescent cancers team (EPICEA), Villejuif, F-94807, France; 2 Université de Paris, France 3 French National Registry of Childhood Haematological Malignancies (RNHE), Villejuif, F-94807, France Corresponding Author : Sophie Bamouni Postal address : Inserm, UMR 1153 CRESS, Epidémiologie des Cancers de l’Enfant et de l’Adolescent (EPICEA), 16 avenue Paul Vaillant Couturier, Hôpital Paul Brousse – Bât Leriche/porte 45, F-94807, Villejuif Cedex, France Telephone : +33 1 45 59 50 37 E-mail address : [email protected] References Amigou, A., Sermage-Faure, C., Orsi, L., Leverger, G., Baruchel, A., Bertrand, Y., Nelken, B., Robert, A., Michel, G., Margueritte, G., Perel, Y., Mechinaud, F., Bordigoni, P., Hémon, D., Clavel, J., 2011. 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J Agric Saf Health 13, 9–24. https://doi.org/10.13031/2013.22308 Supplementary Files AdditionalFigure1.docx AdditionalFigure2.docx Additionaltable1.docx Additionaltable2.docx Additionaltable3.docx Additionaltable4.docx Additionaltable5.docx Additionaltable6.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 Feb, 2022 Reviewers invited by journal 11 Feb, 2022 Editor assigned by journal 08 Feb, 2022 First submitted to journal 03 Feb, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1323671","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":83261759,"identity":"65e805b8-e892-4884-b957-071fe1433c16","order_by":0,"name":"Sophie Bamouni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIie3RPQrCMBTA8QcFp4BrpKJXeCIoouhVWgpxCSq4dHDopJOrFBy8gkdQCp2kXQtZBG+gi0MRE78WSXF0yJ9MgR95SQBMpv+sBA6opcIeee3WfibsTZrFBD4EoveuntQXy/h49GFUXkWxPZmk1XY5is8Z4FhH8JAM0TnAlArG7BAF6YTMW3PATqAjlLeoOwc3ELxlE0kwI02LQ47awUJFbuBuniQhmKYXSVBLIFMkAHf7JDu5uFVI5F0YdWI6bQjmdQl6cjAmB0M9US9Wuc56o5rw9oLk/QGm0cnifsFg8Pg7+nW6HryIyWQymQq6A6n5TWbauBCiAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7267-1589","institution":"INSERM UMR 1153: Centre de Recherche en Epidemiologie et Statistiques Sorbonne Paris Cite","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sophie","middleName":"","lastName":"Bamouni","suffix":""},{"id":83261760,"identity":"381b270f-df76-4512-abf9-7ae008fa3385","order_by":1,"name":"Denis Hémon","email":"","orcid":"","institution":"INSERM UMR 1153: Centre de Recherche en Epidemiologie et Statistiques Sorbonne Paris Cite","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Denis","middleName":"","lastName":"Hémon","suffix":""},{"id":83261761,"identity":"3c4395c8-7701-4c39-b976-22d3dd5806e7","order_by":2,"name":"Laure Faure","email":"","orcid":"","institution":"INSERM UMR 1153: Centre de Recherche en Epidemiologie et Statistiques Sorbonne Paris Cite","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Laure","middleName":"","lastName":"Faure","suffix":""},{"id":83261762,"identity":"ccb1dcb5-a557-45d1-ad5f-26b725d2d2eb","order_by":3,"name":"Jacqueline Clavel","email":"","orcid":"","institution":"INSERM UMR 1153: Centre de Recherche en Epidemiologie et Statistiques Sorbonne Paris Cite","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jacqueline","middleName":"","lastName":"Clavel","suffix":""},{"id":83261763,"identity":"479e44c4-7a99-4c47-884f-989ffe10f61f","order_by":4,"name":"Stéphanie Goujon","email":"","orcid":"","institution":"INSERM UMR 1153: Centre de Recherche en Epidemiologie et Statistiques Sorbonne Paris Cite","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Stéphanie","middleName":"","lastName":"Goujon","suffix":""}],"badges":[],"createdAt":"2022-02-03 10:54:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1323671/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1323671/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":18258892,"identity":"6b1edba6-4b5c-4a85-9f3b-d629bf83cc33","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":64058,"visible":true,"origin":"","legend":"\u003cp\u003eTotal crop density (%) in the French municipalities – Agricultural census\u003cstrong\u003e \u003c/strong\u003e2010\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/973d726a6690037b3b025e28.jpg"},{"id":18258894,"identity":"2befde24-b881-4b79-806a-17eb64bd227e","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62211,"visible":true,"origin":"","legend":"\u003cp\u003eSpecific crop densities (%) in the French municipalities – Agricultural census\u003cstrong\u003e \u003c/strong\u003e2010\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/7aa0ac27f1771857bfef962b.jpg"},{"id":18258893,"identity":"264c4d15-2b5b-4254-8758-a68615df3b0f","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":60428,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized incidence ratio of acute lymphoblastic leukaemia (ALL) in viticulture density\u003csup\u003ea\u003c/sup\u003e categories based on population-weighted semi-quartiles\u003c/p\u003e\u003cp\u003e\u003csup\u003ea \u003c/sup\u003eThe viticulture density in a municipality is defined as the ratio of the total area used for viticulture over the total area of this municipality. The categories were defined as follows: 1\u003csup\u003est\u003c/sup\u003e category: municipalities with a total crop density \u0026lt; 5%, 2\u003csup\u003end\u003c/sup\u003e category: municipalities with viticulture density \u0026lt; 5%, 3\u003csup\u003erd\u003c/sup\u003e to 10\u003csup\u003eth\u003c/sup\u003e categories: groups of municipalities with viticulture density ≥ 5% and equal number of births over the study period (1990-2015)\u003c/p\u003e\u003cp\u003e*p-value of the chi-square test of heterogeneity of the viticulture density categories \u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/80b93a4b4a7495da1ad0059c.jpg"},{"id":18259343,"identity":"381661e3-54fe-4655-8e00-906ecd98924c","added_by":"auto","created_at":"2022-02-15 23:34:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":540623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/ec4ae91d-72da-47db-bd0c-40ce10971d2b.pdf"},{"id":18258896,"identity":"21e5568e-f51a-4bf3-baf9-d30a8c905cd7","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":64711,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/b62537a44e43e1fe9d332d7d.docx"},{"id":18258902,"identity":"b14d622e-c634-461d-886b-20e64efce5a5","added_by":"auto","created_at":"2022-02-15 23:28:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":127627,"visible":true,"origin":"","legend":"","description":"","filename":"AdditionalFigure2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/62c68e55e73733e4ac485f38.docx"},{"id":18258899,"identity":"1aeb73fe-1b88-419f-b0e0-a8518622079a","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":19174,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/4b3b8bde37689510895ef45e.docx"},{"id":18258895,"identity":"ac539299-fb8c-4371-9b5f-9e2d6eba2ca9","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":33063,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/21fbabfa187884623992ef77.docx"},{"id":18259121,"identity":"87baa516-8f71-4bd2-8104-2dafa983331f","added_by":"auto","created_at":"2022-02-15 23:31:03","extension":"docx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":28141,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/06f5e3909e12d78a9a0182b3.docx"},{"id":18259119,"identity":"a611c9b2-446b-47a7-bb66-3d46780c0a5f","added_by":"auto","created_at":"2022-02-15 23:31:03","extension":"docx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":23313,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/fd1afdb43b83bc8b0ccc0f3a.docx"},{"id":18259342,"identity":"14e994cf-a650-4310-8527-7cea31e403f7","added_by":"auto","created_at":"2022-02-15 23:34:03","extension":"docx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":23510,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable5.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/e229b7e2fa2844f6adb42243.docx"},{"id":18258900,"identity":"bad0f6f7-6c83-4498-b6e9-2d2b78be9897","added_by":"auto","created_at":"2022-02-15 23:28:03","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":31329,"visible":true,"origin":"","legend":"","description":"","filename":"Additionaltable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-1323671/v1/37048a06a51cbbc52bb9029b.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eResidential Proximity to Croplands at Birth and Childhood Leukaemia\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute leukaemia (AL), the most frequent childhood cancer, affects around 500 children under 15 each year in France (Poulalhon et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). About 80% of the cases consist in acute lymphoblastic leukaemia (ALL) and 15% in acute myeloid leukaemia (AML). Domestic pesticide use and parental occupational exposure to pesticides are strongly suspected of playing a role in childhood AL. A positive association between childhood AL risk and domestic pesticide use during pregnancy and childhood has been reported in several studies and confirmed by recent meta-analyses (Bailey et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Karalexi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Van Maele-Fabry et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Pooled-analysis meta-analyses by the CLIC consortium have shown associations between maternal occupational exposure during pregnancy and AML risk, and between paternal occupational exposure preconception and childhood ALL (Bailey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Proximity to cropland and agricultural pesticide exposure have been subject to less investigation. Several case-control studies have exploited land-use data (Malagoli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), satellite images (G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) or aerial photographs (Carozza et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) to estimate the agricultural area in the vicinity of the participants\u0026rsquo; geocoded residential addresses, while others, with an ecological design, considered agricultural census data to evaluate the agricultural activity in the area of residence (county or municipality level) (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Walker et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Some studies reported positive associations with particular crop types, with, however, no consistency (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In California, where agricultural pesticide applications are systematically registered with information on the type of pesticides used, quantity applied, crop type and treated area, some studies (Park et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reynolds et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005b\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rull et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found a greater AL risk with several chemical types of pesticide, while in another study no association was observed (Reynolds et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005a\u003c/span\u003e). In Denmark, a cohort study estimated agricultural pesticide exposure around the geocoded addresses of residence during pregnancy based on pesticide sales data and did not identify any association with childhood AL risk (Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn a recent ecological study, we observed a log-linear increase in ALL incidence rate with increasing viticulture density in the municipalities of residence at the time of diagnosis, with a 16% increase in the municipalities in which more than 25% of their area was devoted to viticulture (Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we investigated whether the previously-observed association with viticulture near the residence at the time of diagnosis might reflect a stronger relationship with prenatal exposure. More generally, we tested the hypothesis that the proximity of home to crops and agricultural activities during the prenatal period, a critical period for leukaemogenesis, increased the risk of childhood AL. The study was conducted on the population of children born between 1990 and 2015 in mainland France and took the place of residence at birth as a proxy for residence during the prenatal period.\u003c/p\u003e"},{"header":"2. Population And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Population\u003c/h2\u003e \u003cp\u003eThe population under study consisted in children under 15 born in mainland France between 1990 and 2015. The 19,809,700 births (761,873 per year on average) domiciled in the municipalities of mainland France over the study period were provided by the French National Institute for Statistics and Economic Studies (INSEE). The at-risk populations were estimated assuming that death and migration rates were negligible.\u003c/p\u003e \u003cp\u003eAll the primary AL cases in that population were identified in the French national registry of childhood cancer (RNCE). Of the 9,293 AL cases born and diagnosed in 1990-2015, 269 cases born abroad were excluded. We obtained legal authorisation to access the birth certificates from the town halls of the municipalities of birth (i.e. the municipalities where the maternity hospitals were located) that were recorded in the RNCE. Birth certificates stating the address of the mothers' residences were obtained for 97% of the remaining cases (277 missing addresses). A total of 8,747 AL cases aged under 15 were identified among the children born in 1990-2015 and domiciled at birth in one of the 35,511 municipalities of mainland France. There were 7,236 ALL (6,970 B-cell precursor ALL, BCP-ALL), and 1,335 AML cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Crop density\u003c/h2\u003e \u003cp\u003eThe crop-growing areas of the municipalities were estimated for the birth periods 1990-1994, 1995-2004, and 2005-2015 using data from the 1988, 2000, and 2010 agricultural censuses, respectively (Minist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Crop density was defined as the ratio of the crop area to the total municipality area (about 15 km\u003csup\u003e2\u003c/sup\u003e on average) for each permanent, i.e. viticulture and arboriculture, and non-permanent crop, i.e. straw cereals, maize, rapeseed, sunflowers, fresh vegetables, dry vegetables and protein crops, potatoes, and beet. The methodology was the same as that of the ecological study at the time of diagnosis (Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBriefly, total crop density was considered as a 5-category variable in which the first category was constituted by municipalities with a total crop density of less than 5%, and the others by the population-weighted quartiles of the total crop density. Crop-specific qualitative variables were similar, except for the first category, which was split into a reference category consisting of municipalities with a total crop density of less than 5%, and a category with at least 5% of total crops but less than 5% of the specific crop. For additional analyses on viticulture densities, we split the highest density categories into two semi-quartiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Statistical analysis\u003c/h2\u003e \u003cp\u003eWe used Poisson regression models to test the association between municipality crop densities and AL incidence rates. The number of AL cases expected over the study period was estimated for each birth period (1990-1994, 1995-2004, 2005-2015) and each municipality of residence at birth using national age-specific incidence rates as reference rates. We checked that the annual 0-14-year and age-specific incidence rates were homogeneous over the period 1990-2015 (Additional. Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The analyses were conducted for all AL taken together, and separately for ALL (particularly BCP-ALL) and AML.\u003c/p\u003e \u003cp\u003eThe standardized incidence ratios (SIR) and their 95% confidence intervals were estimated by Poisson regression models. Likelihood ratios were used to test for heterogeneity of SIRs. SIR variations (SIRR) for a 10% increase in crop density were also estimated. The log-linearity hypothesis was tested by likelihood ratio tests between the qualitative model and a semi-quantitative model assigning the population-weighted average value of the crop density to all municipalities in the category.\u003c/p\u003e \u003cp\u003eThe size of urban unit (Paris urban unit \u003cem\u003evs\u003c/em\u003e. other urban units) was systematically included in the exposure-response models for AL, ALL and BCP-ALL in order to improve model fit.\u003c/p\u003e \u003cp\u003eThe statistical significance level was set at 5% for all the tests and the p-values for exposure-response models were estimated using one-sided tests.\u003c/p\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eThe size of the urban unit (Paris urban unit \u003cem\u003evs\u003c/em\u003e. other urban units), residential UV radiation exposure (UV \u0026gt;105.5 J/cm\u003csup\u003e2\u003c/sup\u003e \u003cem\u003evs\u003c/em\u003e. UV \u0026le; 105.5 J/cm\u003csup\u003e2\u003c/sup\u003e) and French deprivation index FDep (Rey et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) (the most deprived quintile Q5 \u003cem\u003evs.\u003c/em\u003e other quintiles Q1-Q4), which were associated with childhood ALL in our previous studies (Marquant et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Coste et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), were considered as potential confounders and adjusted for in the sensitivity analyses conducted on ALL and BCP-ALL. Additional analyses were performed after excluding the Paris urban unit or urban units with a population greater than 100,000, and by stratifying the analyses by age group (0-6 and 7-14 years old). For viticulture, we also performed an analysis for ALL restricted to the 1990-2004 birth period in order to account for a vine uprooting policy introduced in 2007.\u003c/p\u003e \u003cp\u003eAll the analyses were performed using R 3.6.3 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOne third of the births were domiciled in 5.7% of the municipalities with a total crop density lower than 5%. The majority of municipalities had at least 50% cropland (figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The distribution of total crop and specific crop densities weighted by the number of births in the municipalities are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The municipalities with at least 19% total crop area accounted for 50% of births over the study period. The crop specific distributions were asymmetric and varied depending on the type of crop. In particular, for viticulture, very few children (1%) lived at the time of birth in municipalities with a high viticulture density (p99=33.1%). The spatial distribution of specific crop densities is shown in figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Intensive viticulture was mainly practiced in the South-West and Mediterranean regions. Arboriculture is quite rare in France. Straw cereals are the most common type of crop and predominant in the North of France.\u003c/p\u003e\n\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDistribution\u003csup\u003e1\u003c/sup\u003e of the total crop and specific crop densities in the French municipalities\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrop\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP5\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP75\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP95\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP99\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAll crops\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115.7\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePermanent crops\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViticulture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArboriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNon-permanent crops\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw cereals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFresh vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRapeseed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSunflower\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotatoes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e1\u003c/sup\u003eDistribution weighted by numbers of births in the municipalities, over 3 birth periods (based on the 1988, 2000 and 2010 agricultural censuses).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eP5, P25, P50, P75, P95, P99: 5th, 25th, 50th, 75th, 95th and 99th population weighted percentiles of the crop densities (%), respectively.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003csup\u003e2\u003c/sup\u003e 99th percentile is greater than 100% because the cropland areas reported in the agricultural censuses are assigned to the municipality of the farm headquarter, which may not be the municipality where the land plots are situated.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003e3.1. Association between crop densities and childhood AL incidence\u003c/h2\u003e\n \u003cp\u003eWe did not evidence any association between total crop density in the municipality of residence at birth and all AL, ALL and AML (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between childhood leukaemia incidence rate and crop densities\u003csup\u003ea\u003c/sup\u003e (all crops and permanent crops)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crops\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-18.7[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1485.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1228.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e224.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[18.7-37.4[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1485.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1228.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[37.4-61.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1490.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1232.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1497.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1238.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1238.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eViticulture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003eviticulture \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5391.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4459.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e834\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e809.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-10.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[10.0-16.2[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[16.2-24.7[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e117.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e139.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73-1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArboriculture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003earboriculture \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5742.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4748.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e861.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.4[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76-1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.4-9.34[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52-0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[9.4-13.1[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39-1.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79-0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79-0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003eAL: Acute leukaemia; ALL: Acute lymphoblastic leukaemia; AML: Acute myeloid leukaemia; O: Observed number of cases; E: Expected number of cases; SIR: Standardized Incidence Ratio; 95% CI: 95% Confidence Interval;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003ea\u003c/sup\u003e The total crop density and the specific crop density in a municipality were defined as the ratio of the total area used for agriculture and the area used for the specific crop, respectively, over the total area of the municipality (based on national agricultural census data).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003eb\u003c/sup\u003e p-value of the tests (chi-square test of heterogeneity of categories of crop density and test for the regression coefficient in the log-linear Poisson regression model, H0: \u0026beta;\u0026thinsp;\u0026le;\u0026thinsp;0 \u003cem\u003evs\u003c/em\u003e H1: \u0026beta;\u0026thinsp;\u0026gt;\u0026thinsp;0)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003ec\u003c/sup\u003e SIRR (for \u0026Delta; x = 10%) = Relative Standardized Incidence Ratio: multiplicative variation in the SIR for a 10% increase in the crop density derived from a log-linear Poisson regression model, with adjustment on the size of urban unit (Paris vs other urban units) for AL and ALL. The p values for the tests for departure from log-linearity ranged from 0.31 to 0.99.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eFor viticulture, no statistically significant heterogeneity of the SIRs for the density categories was observed for AL or its subgroups; there was no evidence of a log-linear association (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The SIR was slightly higher in the municipalities with the highest viticulture densities (1.10 95%CI [0.94-1.29] for AL) while closer to 1 for the other categories. Although far from being statistically significant, the slight increase for the highest category resembles that observed in our ecological study at the time of diagnosis. We subdivided the viticulture density quartiles into semi-quartiles. There was no change in the log-linear trend parameter estimates, but a greater SIR in the highest density category (municipalities with a viticulture density greater than 37.7%) was detected for AL and ALL (SIR=1.25 95%CI [1.01-1.54] and SIR=1.23 95%CI [0.97-1.55], respectively, figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe arboriculture density analyses did not show any positive association with AL or its subgroups (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe did not observe any heterogeneity of the SIRs for the density categories of non-permanent crops, or any positive log-linear association with AL or its subgroups, ALL and AML (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociation between the incidence rate of childhood leukaemia and non-permanent crop densities\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStraw cereals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003estraw cereals \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2290.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1893.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e343.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5.0-8.8[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e912.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e754.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[8.8-14.2[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e910.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e796\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e753.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[14.2-23.8[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e919.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e760.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e925.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e765.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003emaize \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4666\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4612.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3815.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e693.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-7.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e275.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7.03-9.8[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e332.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e274.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[9.83-14.4[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e335.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e285.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRapeseed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003erapeseed \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5388.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4456.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.1[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76-1.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.1-7.9[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e140.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7.9-11.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSunflowers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5%\u003c/p\u003e\n \u003cp\u003eand sunflowers \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5600.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4631.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e840.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.2[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.2-8.2[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76-1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[8.2-11.9[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67-1.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.62-1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92-1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86-1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePotatoes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003epotatoes \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5792.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4790.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e869.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.3[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91-1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24-1.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.3-8.5[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[8.47-12.6[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54-2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;12.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81-1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78-1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFresh vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003efresh vegetables \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5729.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4738.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e859.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.11[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85-1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.54-2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.11-7.7[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51-0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24-1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7.7-11.5[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80-1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72-1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81-2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97-1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94-1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry vegetables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003edry vegetables \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5687.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4703.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e853.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-5.9[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.73-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.63-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5.9-7.2[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68-1.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65-1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60-1.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7.23-9.8[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79-1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78-1.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.53-1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75-2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78-1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2788.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2308.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal crop density \u0026ge;5% and\u003c/p\u003e\n \u003cp\u003ebeets \u0026lt;5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5545.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4586.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98-1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e832.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96-1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[5-6.9[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71-1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70-1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[6.9-9.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e103.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75-1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77-1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44-1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[9.04-13.0[\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79-1.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84-2.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77-1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.76-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.59-1.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSIRR for ∆x= 10%\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.86-1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83-1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003eAL: Acute leukaemia; ALL: Acute lymphoblastic leukaemia; AML: Acute myeloid leukaemia; O: Observed number of cases; E: Expected number of cases; SIR: Standardized Incidence Ratio; 95% CI: 95% Confidence Interval;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003ea\u003c/sup\u003e The crop density is defined as the ratio of the area used for the crop over the total area of the municipality (based on national agricultural census data).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003eb\u003c/sup\u003e p-value of the tests (chi-square test of heterogeneity of SIRs of agricultural density categories, test for the regression coefficient in the log-linear Poisson regression model, H0:\u0026beta;\u0026thinsp;\u0026le;\u0026thinsp;0 \u003cem\u003evs\u003c/em\u003e H1: \u0026beta;\u0026thinsp;\u0026gt;\u0026thinsp;0)\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\"\u003e\u003csup\u003ec\u003c/sup\u003e SIRR (for \u0026Delta; x = 10%)= Relative Standardized Incidence Ratio: multiplicative variation in the SIR for a 10% increase in the crop density derived from of a log-linear Poisson regression model, with adjustment on the size of urban unit (Paris \u003cem\u003evs\u003c/em\u003e other urban units) for AL and ALL. The p-values for the tests for departure from log-linearity ranged from 0.08 to 0.92.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.2. Sensitivity and additional analyses\u003c/h2\u003e\n \u003cp\u003eWe examined the associations between ALL incidence rate and the potential ecological confounders by fitting a Poisson regression model with each variable separately (Additional. Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The size of the urban unit was associated with ALL incidence rate, with an 11% decrease in ALL incidence rate in the population of children living in the Paris urban unit at birth, compared to other urban units (SIRR=0.89 95%CI [0.84-0.95]). ALL incidence rate also tended to be lower for children living in the most deprived municipalities, although statistical significance was not attained (SIRR=0.96 95%CI [0.91-1.02]). As expected from our previous studies, residential UV radiation was positively associated with ALL (SIRR=1.06 95%CI [1.01-1.12]).\u003c/p\u003e\n \u003cp\u003eThese factors were also associated with crop densities (Additional. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). A large majority (83%) of children who lived in the Paris urban unit at birth was located in municipalities with less than 5% cropland, compared to 20% for the other urban units. For almost all crops, less than 0.5% of children in the Paris urban unit lived in municipalities in the highest crop density category. The categories with viticulture density \u0026ge; 5% were more frequent in the municipalities with UV radiation \u0026gt;105.5 J/cm\u003csup\u003e2\u003c/sup\u003e, with around 4% of the at-risk population in each category \u003cem\u003evs\u003c/em\u003e \u0026lt;1% in municipalities with UV radiation \u0026le; 105.5 J/cm\u003csup\u003e2\u003c/sup\u003e. The same association was observed for arboriculture and sunflower densities. In contrast, UV radiation level tended to be negatively associated with rapeseed, dry vegetable and beet densities. The most deprived municipalities had a lower proportion of at-risk population in the first total crop density category. For specific crop densities, the distribution of the at-risk population in the last 4 categories was very similar in the most deprived municipalities and in other municipalities, except for straw cereals. In sensitivity analyses on AL and ALL, we adjusted for residential UV radiation exposure and FDep 2006 index of the municipalities in addition to the size of urban unit already included in the regression models. The results did not differ from those of the main analyses (Additional. Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAnalyses performed by excluding the Paris urban unit, or municipalities from the most populated urban units (population \u0026gt; 100,000) did not change the results (Additional. Table 4).\u003c/p\u003e\n \u003cp\u003eWhen we stratified the analyses by two age groups (0-6 and 7-14 years old), we found a significant positive log-linear association between rapeseed density and ALL incidence among 7-14-year olds (SIRR=1.29 95%CI [1.12-1.49] per 10% increase in rapeseed density, Additional. Table 5). The numbers of cases in the municipalities with the highest rapeseed densities were, however, quite limited (25-30 expected cases) and the SIRs were heterogeneous, with values greater than one in the third and last categories (Additional. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For the other crops, the results remained stable by age-group (Additional. Table 5). The analyses with viticulture restricted to children born during the period 1990-2004 resulted in the same conclusion (data not shown).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe investigated the association between total and specific-crop densities in the municipalities of residence at birth and childhood leukaemia incidence rates in the population of children born in 1990-2015. In all, we did not observe any statistically significant positive association between total childhood AL, ALL and AML incidence rates and any crop type. Interestingly, the incidence rate of AL was slightly higher in children living at birth in the municipalities with the highest viticulture densities (SIR=1.25 95%CI [1.01-1.54] in the highest semi-quartile of exposure), which was in line with our study on residences at diagnosis (Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the same methodology and same agricultural data, we evidenced a log-linear increase in ALL incidence rate over the period 1990-2014 with increasing viticulture density in the municipality of residence at diagnosis (SIRR=1.04 95%CI [1.00-1.06] per 10% increase) and an increase in ALL incidence rate in the municipalities with the highest viticulture density (i.e. with more than a quarter of their area used for viticulture, SIR=1.17 95%CI [1.01-1.35]) (Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although the result of the present study is not statistically significant, and less marked than in our previous study, it provides some support for the hypothesis that there is a positive association between childhood AL incidence and viticulture density in the municipalities. However, the weakness of the association does not suggest that our previous results can be explained by prenatal exposures.\u003c/p\u003e \u003cp\u003eViticulture is all the more of interest in that vines are a permanent crop subject to many pesticide treatments, particularly fungicidal treatments. Few studies have investigated the role of residential exposure to agricultural pesticides in the occurrence of childhood AL using proximity to croplands as a surrogate (Additional. Table 6). Most of the studies were conducted in the USA. With a large number of cases (6,168 AL), Carozza et al. (Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) considered the percentage cropland devoted to farming at the county level and showed a positive association between total cropland (\u0026ge;60% of county total acreage \u003cem\u003evs\u003c/em\u003e \u0026lt; 20%) and ALL and AML risk (OR=1.3 95%CI [1.1-1.4], and OR=1.8 95% CI [1.4-2.3], respectively). The authors also reported positive associations with some specific crops (maize and soya bean densities for AML, oats density for ALL). In a study by Booth et al. (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) conducted in 6 Midwestern states, 0-4-year AL and ALL, incidence rates were associated with dry beans and sugar beet in exposure-response analyses. A positive association between 0-4-year AML incidence rates and oats density was also reported. More recently, a study conducted in California reported a greater ALL risk in children residing close to plant nurseries at birth (OR for \u0026lt;75 m \u003cem\u003evs\u003c/em\u003e \u0026ge; 600 m of 3.09 95% CI [1.14-8.34] (Nguyen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)). Conversely, three Texan studies (Carozza et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Thompson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Walker et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) did not evidence any association between childhood AL and agricultural area in the county of residence at birth (Thompson et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Walker et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) or within a 1000-m buffer around the geocoded address of residence at birth (Carozza et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In addition to our previous ecological study (Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), three studies have been conducted in Europe (G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Malagoli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In Italy, in a case-control study with 111 AL cases and 444 matched controls, Malagoli et al. did not find any association between AL risk and arable crop, orchard, vineyard or vegetable densities (Malagoli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), while, in Spain, Gomez-Barroso et al. reported an elevated risk of leukaemia (1,062 cases) with total crop density and several specific crop densities (arable land or permanently irrigated land; rice fields; heterogeneous agricultural areas) (G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study also found strong associations with other childhood cancer types; it is unclear whether those results may be due to different addresses being used for the cases (at diagnosis) and controls (at birth). A recent Danish cohort study (Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found a 2-fold increase in leukaemia risk (61 AL cases) for children whose mothers lived close to agricultural areas during pregnancy (\u0026ge; 24 ha of total agricultural land within a 500-m buffer), in particular when grass/clover, peas and maize crops were present.\u003c/p\u003e \u003cp\u003eSeveral studies thus reported positive associations between childhood AL risk and proximity to cropland, particularly around diagnosis (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Coste et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Malagoli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the estimates of the reported associations were sometimes imprecise due to the limited numbers of cases (G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Malagoli et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In addition, the positive associations reported in several studies were for total crop density (Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; G\u0026oacute;mez-Barroso et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Patel et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), which may not be a relevant surrogate for pesticide exposure because of the heterogeneity of the crops included, especially in terms of pesticide use. It is difficult to compare our results with those from other countries because of differences in agricultural areas and practices (FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, in the USA, positive associations were reported with dry beans, sugar beet (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), oats (Booth et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), maize and soya beans (Carozza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which are less common crops in France (FAO, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe used agricultural census data to estimate crop densities in the municipalities of residence at birth, as indicators of potential exposure to agricultural pesticides. A limitation consists in the fact that the census locates the crops in the municipality of the farm headquarters, while the crops may be located in neighbouring municipalities. However, the resulting misclassifications most likely affect similar agricultural areas close to each other and preserve contrasts between exposed and unexposed municipalities. Another limitation is that we assigned the agricultural census closest to the year of birth, which may have induced misclassification for rotational crops when the years of census and birth differed. This limitation should not affect the classification of permanent crops like vineyards and arboriculture. Moreover, the results remained unchanged in the sensitivity analyses restricted to children born in 1990-2004, before many vines were uprooted.\u003c/p\u003e \u003cp\u003eThe agricultural censuses do not distinguish organically-farmed fields from conventionally-farmed fields. However, organically-farmed fields constitute only a small fraction of the agricultural area of France; they accounted for 9.5% of the total agricultural area in 2019 (Minist\u0026egrave;re de agriculture et de l\u0026rsquo;alimentation, 2021) and probably less during our study period since organic farming was less widespread. Moreover, two surveys of agricultural practices showed that more than 90% of the areas covered by the crops we considered in our study had received at least one pesticide treatment in 2006 and 2011 (Minist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdjustments for the degree of urbanization, deprivation and UV radiation, which were associated with childhood ALL incidence rate at the municipality level in our previous studies, did not change the result. We were unable to take individual factors like domestic pesticide use and parental agricultural occupation into account. Even if those factors are more prevalent in the most agricultural municipalities, the proportion of children with a parent occupationally exposed to pesticides is very low in France (Bailey et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Maternal use of pesticides during pregnancy is much more common, about 35% for control mothers in two French case-control studies (Mavoungou et al., 2020; Rios et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rudant et al., 2007; Vidart d\u0026rsquo;Egurbide Bagazgo\u0026iuml;tia et al., 2018) but not restricted to agricultural areas. Residential exposure to traffic-related air pollution, which was associated with the risk of childhood AML (Amigou et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Houot et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), could not be accounted for. However, the results were unchanged after exclusion of the municipalities in urban units with a population greater than 100,000, where those associations were observed.\u003c/p\u003e \u003cp\u003eOur study has several strengths. A major asset is that our findings are based on a large number of cases, identified from a population-based registry. The high degree of completeness, avoided selection biases, and the high standard of diagnosis classifications and high reliability of addresses (3% missing addresses) minimised misclassifications. Another asset is the use of agricultural census data, collected on an exhaustive basis and providing the detailed distribution of ten types of crops on the fine scale of the municipality for the entire country. The crop types are known to be quite different in terms of average annual number of pesticide treatments, percentage of area treated, and main target pesticide, with variations between time periods and regions (Minist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). A few Californian studies benefitted from the Pesticides Use Reporting (PUR) system, with a large database on pesticides applied to crops in the state of California, to investigate for associations with childhood leukaemia (Park et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Reynolds et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005b\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005a\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Rull et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Some positive associations with specific substances or classes of pesticide have been reported, with, however, heterogeneous results: AL risk was thus associated with high use of propargite near the residence at diagnosis in Reynold et al. 2002 (Reynolds et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); a moderate lifetime averaged use of insecticides and fumigants in the vicinity of the addresses of residence in Rull et al. (Rull et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); the use of metam sodium and dicofol near the residence at birth for children aged 0-4 years in Reynold et al. 2005 (Reynolds et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005b\u003c/span\u003e); and the uses of any carcinogenic pesticide, several chemical classes or individual pesticides near the residence at birth for children aged less than 6 years in Park et al. (Park et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur next step will be to conduct a large case-control study using geocoded addresses and a geographical information system in order to evaluate, as precisely as possible, the presence of cropland in the vicinity of the residential addresses, with a particular focus on viticulture. Elucidating the relationship between the ecological and individual crop proximity indicators will be of great importance. Future challenges will then consist in enhanced assessment of the role of agricultural pesticide exposure and identification of the specific substances potentially involved in childhood leukaemia.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe present study did not evidence statistically significant associations between the density of crops in the municipality of birth and the incidence rates of childhood AL. The slightly higher ALL incidence rate in children born in the municipalities with the highest viticulture densities may reflect the association that we previously observed at diagnosis. But, overall, our results do not support the hypothesis that prenatal exposure to neighbouring agricultural activity, particularly viticulture, plays a role in childhood leukaemia.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research undertaken with the RNCE data is covered by agreements on the ethical use of data and the protection of personal data, and have been approved by French national authorities (French data protection authority CNIL No. 998198).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and programs supporting the results of the study have been archived by the Inserm EPICEA team, in compliance with the general data protection regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR software code supporting the results of the study have been archived by the Inserm EPICEA team, in compliance with the general data protection regulation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe project was supported by the French National Cancer Institute (INCa); the French National Institute of Health and Medical Research (INSERM); the French National Research Program for Environmental and Occupational Health of the Anses with the support of the Cancer TMOI of the French National Alliance for Life and Health Sciences (AVIESAN) (EST-2016/1/161) and Sant\u0026eacute; Publique France. Sophie Bamouni also received funding from Fondation de France (grant number 00089810) for her PhD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSB\u003c/strong\u003e: Methodology, Formal analysis, Writing-Original draft preparation, Review and Editing; \u003cstrong\u003eDH\u003c/strong\u003e: Conceptualization, Methodology, Supervision, Writing-Review and Editing; \u003cstrong\u003eLF\u003c/strong\u003e: Data Curation, Writing-Review and Editing; \u003cstrong\u003eJC\u003c/strong\u003e: Conceptualization, Methodology, Supervision, Writing-Review and Editing, Project administration; \u003cstrong\u003eSG\u003c/strong\u003e: Conceptualization, Methodology, Supervision, Writing-Review and Editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are grateful to the staff of the French National Registry of Childhood Cancer (RNCE), who collected the incidence data. The authors also thank the paediatric oncology teams for their help in data collection. The authors thank ADISP (Archives de Donn\u0026eacute;es Issues de la Statistique Publique) for providing birth data from the National Institute of Statistics and Economic Studies INSEE (application form No. 19728).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo;informations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors list:\u0026nbsp;\u003c/strong\u003eSophie Bamouni\u003csup\u003e1,2\u0026nbsp;\u003c/sup\u003e(0000-0002-7267-1589), Denis H\u0026eacute;mon\u003csup\u003e1,2\u003c/sup\u003e, Laure Faure\u003csup\u003e1,2,3\u003c/sup\u003e, Jacqueline Clavel\u003csup\u003e1,2,3 (\u003c/sup\u003e0000-0002-3616-7676), St\u0026eacute;phanie Goujon\u003csup\u003e1,2,3\u0026nbsp;\u003c/sup\u003e(0000-0002-4149-2246).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Inserm, UMR 1153 Center of Research in Epidemiology and StatisticS (CRESS), Epidemiology of childhood and adolescent cancers team (EPICEA), Villejuif, F-94807, France;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Universit\u0026eacute; de Paris, France\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e French National Registry of Childhood Haematological Malignancies (RNHE), Villejuif, F-94807, France\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e: Sophie Bamouni\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePostal address\u003c/strong\u003e : Inserm, UMR 1153 CRESS, Epid\u0026eacute;miologie des Cancers de l\u0026rsquo;Enfant et de l\u0026rsquo;Adolescent\u0026nbsp;(EPICEA), 16 avenue Paul Vaillant Couturier, H\u0026ocirc;pital Paul Brousse \u0026ndash; B\u0026acirc;t Leriche/porte 45, \u0026nbsp; F-94807, Villejuif Cedex, France\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTelephone\u003c/strong\u003e: +33 1 45 59 50 37\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE-mail address\u003c/strong\u003e:\u0026nbsp;\u003ca href=\"about%3Ablank\"\[email protected]\u003c/a\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAmigou, A., Sermage-Faure, C., Orsi, L., Leverger, G., Baruchel, A., Bertrand, Y., Nelken, B., Robert, A., Michel, G., Margueritte, G., Perel, Y., Mechinaud, F., Bordigoni, P., H\u0026eacute;mon, D., Clavel, J., 2011. 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Int J Hyg Environ Health 219, 742\u0026ndash;748. https://doi.org/10.1016/j.ijheh.2016.09.015\u003c/li\u003e\n \u003cli\u003eMarquant, F., Goujon, S., Faure, L., Guissou, S., Orsi, L., H\u0026eacute;mon, D., Lacour, B., Clavel, J., 2016. Risk of Childhood Cancer and Socio-economic Disparities: Results of the French Nationwide Study Geocap 2002-2010. Paediatr Perinat Epidemiol 30, 612\u0026ndash;622. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1111/ppe.12313\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMavoungou, S., Rios, P., Pacquement, H., Nolla, M., Rigaud, C., Simonin, M., Bertrand, Y., Lambilliotte, A., Faure, L., Orsi, L., Clavel, J., Bonaventure, A., 2020. Maternal exposure to pesticides and risk of childhood lymphoma in France: A pooled analysis of the ESCALE and ESTELLE studies (SFCE). Cancer Epidemiol 68, 101797. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1016/j.canep.2020.101797\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eMinist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, 2021. Infographie-L\u0026rsquo;agriculture biologique en France.\u0026nbsp;URL https://agriculture.gouv.fr/infographie-lagriculture-biologique-en-france (accessed 7.21.21).\u003c/li\u003e\n \u003cli\u003eMinist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, 2014. Agreste, la statistique agricole-Pratiques culturales 2011.\u0026nbsp;URL https://agreste.agriculture.gouv.fr/agreste-web/disaron/Dos21/detail/ (accessed 11.15.21).\u003c/li\u003e\n \u003cli\u003eMinist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, 2012. Agreste-Recensements agricoles. URL https://www.agreste.agriculture.gouv.fr/agreste-web/disaron/G_2012/detail/ (accessed 1.3.22).\u003c/li\u003e\n \u003cli\u003eMinist\u0026egrave;re de l\u0026rsquo;agriculture et de l\u0026rsquo;alimentation, 2010. Agreste, la statistique agricole-Pratiques culturales 2006.\u0026nbsp;URL https://agreste.agriculture.gouv.fr/agreste-web/disaron/Dos8/detail/ (accessed 11.15.21).\u003c/li\u003e\n \u003cli\u003eNguyen, A., Crespi, C.M., Vergara, X., Chun, N., Kheifets, L., 2021.\u0026nbsp;Residential proximity to plant nurseries and risk of childhood leukemia. Environ Res 200, 111388. https://doi.org/10.1016/j.envres.2021.111388\u003c/li\u003e\n \u003cli\u003ePark, A.S., Ritz, B., Yu, F., Cockburn, M., Heck, J.E., 2020. Prenatal pesticide exposure and childhood leukemia - A California statewide case-control study. Int J Hyg Environ Health 226, 113486. https://doi.org/10.1016/j.ijheh.2020.113486\u003c/li\u003e\n \u003cli\u003ePatel, D.M., Gyldenk\u0026aelig;rne, S., Jones, R.R., Olsen, S.F., Tikellis, G., Granstr\u0026ouml;m, C., Dwyer, T., Stayner, L.T., Ward, M.H., 2020. Residential proximity to agriculture and risk of childhood leukemia and central nervous system tumors in the Danish national birth cohort.\u0026nbsp;Environ Int 143, 105955. https://doi.org/10.1016/j.envint.2020.105955\u003c/li\u003e\n \u003cli\u003ePoulalhon, C., Vignon, L., Idbrik, L., Bernier-Chastagner, V., Fabre, M., Schleiermacher, G., Dijoud, F., Perrin, C., Varlet, P., Faure, L., Guissou, S., D\u0026eacute;sandes, E., H\u0026eacute;mon, D., Berger, C., Lacour, B., Clavel, J., 2020.\u0026nbsp;Data Resource Profile: The French Childhood Cancer Observation Platform (CCOP). International Journal of Epidemiology 49, 1434\u0026ndash;1435k. https://doi.org/10.1093/ije/dyaa048\u003c/li\u003e\n \u003cli\u003eRey, G., Jougla, E., Fouillet, A., H\u0026eacute;mon, D., 2009. Ecological association between a deprivation index and mortality in France over the period 1997 \u0026ndash; 2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health 9, 33. https://doi.org/10.1186/1471-2458-9-33\u003c/li\u003e\n \u003cli\u003eReynolds, P., Von Behren, J., Gunier, R., Goldberg, D.E., Hertz, A., 2005a. Agricultural pesticides and lymphoproliferative childhood cancer in California. Scand J Work Environ Health 31 Suppl 1, 46\u0026ndash;54; discussion 5-7.\u003c/li\u003e\n \u003cli\u003eReynolds, P., Von Behren, J., Gunier, R.B., Goldberg, D.E., Harnly, M., Hertz, A., 2005b. Agricultural pesticide use and childhood cancer in California. Epidemiology 16, 93\u0026ndash;100. https://doi.org/10.1097/01.ede.0000147119.32704.5c\u003c/li\u003e\n \u003cli\u003eReynolds, P., Von Behren, J., Gunier, R.B., Goldberg, D.E., Hertz, A., Harnly, M.E., 2002. Childhood cancer and agricultural pesticide use: an ecologic study in California.\u0026nbsp;Environ Health Perspect 110, 319\u0026ndash;324.\u0026nbsp;\u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1289/ehp.02110319\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eRios, P., Bailey, H.D., Lacour, B., Valteau-Couanet, D., Michon, J., Bergeron, C., Boutroux, H., Defachelles, A.-S., Gambart, M., Sirvent, N., Thebaud, E., Ducassou, S., Orsi, L., Clavel, J., 2017. Maternal use of household pesticides during pregnancy and risk of neuroblastoma in offspring. A pooled analysis of the ESTELLE and ESCALE French studies (SFCE). Cancer Causes Control 28, 1125\u0026ndash;1132. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1007/s10552-017-0944-5\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eRudant, J., Menegaux, F., Leverger, G., Baruchel, A., Nelken, B., Bertrand, Y., Patte, C., Pacquement, H., V\u0026eacute;rit\u0026eacute;, C., Robert, A., Michel, G., Margueritte, G., Gandemer, V., H\u0026eacute;mon, D., Clavel, J., 2007. Household exposure to pesticides and risk of childhood hematopoietic malignancies: The ESCALE study (SFCE). Environ Health Perspect 115, 1787\u0026ndash;1793. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1289/ehp.10596\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eRull, R.P., Gunier, R., Von Behren, J., Hertz, A., Crouse, V., Buffler, P.A., Reynolds, P., 2009. Residential proximity to agricultural pesticide applications and childhood acute lymphoblastic leukemia.\u0026nbsp;Environ Res 109, 891\u0026ndash;899. https://doi.org/10.1016/j.envres.2009.07.014\u003c/li\u003e\n \u003cli\u003eThompson, J.A., Carozza, S.E., Zhu, L., 2008.\u0026nbsp;Geographic risk modeling of childhood cancer relative to county-level crops, hazardous air pollutants and population density characteristics in Texas. Environ Health 7, 45. https://doi.org/10.1186/1476-069X-7-45\u003c/li\u003e\n \u003cli\u003eVan Maele-Fabry, G., Gamet-Payrastre, L., Lison, D., 2019. Household exposure to pesticides and risk of leukemia in children and adolescents: Updated systematic review and meta-analysis. Int J Hyg Environ Health 222, 49\u0026ndash;67. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1016/j.ijheh.2018.08.004\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eVidart d\u0026rsquo;Egurbide Bagazgo\u0026iuml;tia, N., Bailey, H.D., Orsi, L., Lacour, B., Guerrini- Rousseau, L., Bertozzi, A.-I., Leblond, P., Faure-Conter, C., Pellier, I., Freycon, C., Doz, F., Puget, S., Ducassou, S., Clavel, J., 2018. Maternal residential pesticide use during pregnancy and risk of malignant childhood brain tumors: A pooled analysis of the ESCALE and ESTELLE studies (SFCE). Int J Cancer 142, 489\u0026ndash;497. \u003ca href=\"about%3Ablank\"\u003ehttps://doi.org/10.1002/ijc.31073\u003c/a\u003e\u003c/li\u003e\n \u003cli\u003eWalker, K.M., Carozza, S., Cooper, S., Elgethun, K., 2007. Childhood cancer in Texas counties with moderate to intense agricultural activity. J Agric Saf Health 13, 9\u0026ndash;24. https://doi.org/10.13031/2013.22308\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"childhood leukaemia, agricultural crops, agricultural census, pesticides, prenatal exposure","lastPublishedDoi":"10.21203/rs.3.rs-1323671/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1323671/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDomestic and parental occupational pesticide exposures are suspected of involvement in the occurrence of childhood acute leukaemia (AL), but the role of exposure to agricultural activities is little known. In a previous ecological study conducted in France, we observed an increase in acute lymphoblastic leukaemia (ALL) incidence rate with increasing viticulture density in the municipalities of residence at diagnosis.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003ethis study aimed to test the hypothesis that residential proximity to croplands at birth increases the risk of childhood AL, with a particular focus on vineyards.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe identified all the primary AL cases diagnosed before the age of 15 years in the cohorts of children born in the French municipalities between 1990 and 2015. We estimated crop densities in each municipality of residence at birth using agricultural census data, for ten crop types. Variations in standardized incidence ratios (SIR) were evaluated with Poisson regression models, for all AL, ALL and acute myeloid leukaemia (AML), separately.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 19,809,700 children born and residing in mainland France at birth in 1990-2015, 8,747 AL cases (7,236 ALL and 1,335 AML) were diagnosed over the period. We did not evidence any statistically significant positive association between total crop density or any specific crop density in the municipality of residence at birth and all AL, ALL or AML. Interestingly, we observed a higher ALL incidence rate in the municipalities with the highest viticulture densities (SIR=1.25 95%CI [1.01-1.54]). Adjusting for the main potential confounders did not change the results.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study does not support the hypothesis that residential proximity to croplands, particularly vineyards, around birth plays a role in childhood leukaemia. The slightly higher ALL incidence rate in children born in the municipalities with the highest viticulture densities may reflect the previously-observed association at diagnosis.\u003c/p\u003e","manuscriptTitle":"Residential Proximity to Croplands at Birth and Childhood Leukaemia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-02-15 23:28:01","doi":"10.21203/rs.3.rs-1323671/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2022-02-18T13:23:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-02-11T20:36:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-02-08T05:31:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Health","date":"2022-02-03T05:53:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enhe","sideBox":"Learn more about [Environmental Health](http://ehjournal.biomedcentral.com)","snPcode":"12940","submissionUrl":"https://submission.nature.com/new-submission/12940/3","title":"Environmental Health","twitterHandle":"@bmc","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e9cb91e-2637-4208-b077-40391ae275e3","owner":[],"postedDate":"February 15th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-10-13T07:46:55+00:00","versionOfRecord":[],"versionCreatedAt":"2022-02-15 23:28:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1323671","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1323671","identity":"rs-1323671","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}