Trends and Spatial Patterns of Sorghum and Millet Production in the Northern Savannah Agro-climatic Zone of Ghana; A statistical Appraisal

Trends and Spatial Patterns of Sorghum and Millet Production in the Northern Savannah Agro-climatic Zone of Ghana; A statistical Appraisal | 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 Trends and Spatial Patterns of Sorghum and Millet Production in the Northern Savannah Agro-climatic Zone of Ghana; A statistical Appraisal Steve Ampofo, Jeffrey N. A. Aryee, Thompson Annor, Leonard K. Amekudzi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5508540/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sorghum and Millet are the two most essential kinds of cereal, among many others, grown in the Northern Savannah Climatic Zone (NSAZ) of Ghana. The zone has climatic and ecological conditions which are well- suited to the cultivation of these crops. The high dependence on the two crops as major staples has ensured that they become essential crops whose production results in food security. However, output for these crops has suffered challenges emanating from unpredictable climatic conditions and high cost of inputs. This paper investigates and compares the level of productivity and rates of change for the two crops between the period 1960 and 2015 using the Mann-Kendall (MK) trend analysis. An assessment of the spatial dynamics that identifies areas of high production between the period 1992 and 2015 carried out using ordinary kriging interpolation. Sorghum production is at a much higher level of output with a maximum production level of 387,400 metric tonnes in the year 1997 compared to the maximum of Millet which is at 245,550 metric tonnes in the year 2008. The seasonal analysis reveals a very high increase in output for both crops for the period 1990 to 2010 (season 4) and the lowest season of production from 1970–1980 (season 2). Notably, the North-Eastern part of the Upper East region around the Bawku East, Bawku West and the Garu-Tempane districts are the areas with the highest levels of output which lies between latitudes 10° and 11°N which are the areas much drier. The general levels of productivity reveal that both crops have increased in output over the period of study, but Sorghum has seen a much higher rate of growth than Millet. The increase and decrease in production associated with a complimentary rise and reduction in Yield and Area harvested. The general spatial pattern of high Millet and Sorghum output and Yield varies over the entire Northern Savanah Agro-climatic Zone but with higher results recorded for the higher latitudes. Sorghum Millet Trend Pattern Mann-Kendall Yield Savannah Ghana Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1.0 Introduction There is substantial evidence to show the effects of climate change on terrestrial food production in several regions [ 1 , 2 ]. There are challenges to ensure continued increases in agricultural output to meet growing food demands globally as a result of the potential impact of climate change and variability [ 3 ] and this is also true for much of West Africa (Roudier et al., 2011). Regions in high latitudes have experienced increases in food production whiles those in the low latitudes have experienced decreases [ 3 ]. These studies on climate change are mostly at the regional scale and have often not considered local needs, output and variation, but on crops that have international significance [ 4 ]. Sorghum and Millet are the two most essential cereals among many others grown in the Northern Savannah Climatic Zone (NSAZ) as the Agro-climatic zone have climatic and ecological conditions which are well suited to these crops. The location described as the food basket of Ghana is the leading supplier of cereals. Sorghum and Millet are essential staples in the diet of indigenes of the three regions in the zone and are used in most traditional meals. However, climate change and variability pose a severe threat to the productivity of these crops, because there is an increasing trend of farmers who though prefer Sorghum and Millet are now shifting to the cultivation of other crops such as maize (Zea Mays) and sweet potato [ 5 ]. The climate of the northern Savannah Agro-climatic Zone has undergone some variation which affects farmer’s ability to forecast and also expectations to engage in rain-fed agriculture [ 6 , 7 , 8 , 9 , 10 ]. The period of inadequate food provisioning defined as the number of months between stock depletion and the next harvest for Sorghum and Millet for the three regions of the zone is at least five months [ 11 ] with severe socio-economic implications creating food insecurity [ 12 , 13 , 14 ] and concomitant effects of seasonal migration [ 15 ] and health [ 16 ]. The response to the impact of climate variability and change on agriculture has been the expansion of farmlands into marginal Savannah lands and forest zones, and this phenomenon is only constrained in extent by the spatial heterogeneity in the agroecology, cultural and socio-economic conditions [ 17 ]. Sorghum (Sorghum Bicolor (L.) Moench) is of tropical origin also referred to variously as Durra, Jowari, or Milo but can grow in both temperate and tropical zones. Sorghum is a grass specie grown as food for humans, animal feed, and also for ethanol production. It originated from Africa and is the world’s fifth-largest cultivated cereal after rice, wheat, maize and barley grown mostly in tropical and sub-tropical regions. It is an annual crop that grows in clumps that may reach over 4m high at maturity. It has high soil fertility requirements and increased drought tolerance with a minimum root depth of 12 inches thriving on many marginal sites. Its unique physiology makes it one of the toughest of all cereals. Sorghum is one of the quickest maturing food plants (certain types can grow in as little as 75 days and can provide three harvests a year). Grain size is small and ranges from 2 to 4 mm in diameter, and it is hardly different from wheat and maize in nutrients composition which is 70% carbohydrate, 12 per cent protein, 3%t fat, 2% fibre, and 1.5% ash. Sorghum has a minimum temperature requirement of 8.33 (°C) [ 18 , 19 ]. Pearl Millet ( Pennisetum glaucum (L.) R.Br .) is a food crop grown widely in Africa and India. It is used for a variety of food products and local beer. It has early maturation and highly valued by farmers because of its efficient use of water, adaptation to very arid conditions and nutrient-poor sandy soils, drought-resistant, less tolerant of waterlogging and flooding compared to Sorghum. This leafy grass plant also called cattail, bulrush and candle Millet has a sowing window between the months of May to September. Germination and emergence of the plant occur five days after planting and proliferates within 14 days before reaching the next phase of flowering within 60 to 70 days from growing. In northern Ghana, although the rainfall is unimodal, it is possible to have two growing cycle for Millet due to its early maturation [ 19 ]. The challenge of ensuring food security for all the indigenes of the NSAZ, and reinforcing the notion of the NSAZ as the food basket for the entire country particularly with regards to the supply of grains and cereals makes it imperative to investigate agricultural productivity levels for Sorghum and Millet. This task is even more relevant considering that it covers about a third of the size of Ghana yet has a unimodal rainfall regime which makes it prone the impacts of climate variability and change. This paper seeks to assess the effects of climate change and climate variability on the production of Millet and Sorghum. Additionally, this paper identifies and map out the areas of high concentration of production and relate the spatial output levels to the latitudinal change. 2.0 Materials and Methods 2.1 Study area Ghana is located in Southern coast of West Africa bounded to the east by Togo, the west by La Cote D’ivoire, the north by Burkina Faso and south by the Atlantic Ocean referred to as the Gulf of Guinea. It lies between latitudes 4° 44’ N and 11° 11’N and longitudes 3° 11’ W and 1° 11’ E. It is composed of four (4) distinct Agro-climatic zones (Fig. 1 ), which differ in terms of the climatic variables of seasons, temperature, rainfall amount, and rainy season characteristics [ 20 ]. The Northern Savannah Agro-climatic Zone is the largest of the four zones with an area of approximately 87,584 km² in size covering the entirety of the three (3) administrative regions of Upper East, Upper West and the erstwhile Northern region (Table 1 ), which is now decomposed into the Savannah, North-East and Northern regions. It is composed of the Guinea Savannah and Sudan savannah vegetation types. Guinea and the Sudan savannah vegetation in this Agro-climatic zone cover 37% of the total landmass of Ghana and have an average annual rainfall of 1050 mm per year. Observed differences between these two ecological formations are the length of the growing season which is 180–200 days for the Guinea savannah and 150–160 days for the Sudan savannah ( http://www.fao.org/fileadmin/user_upload/aquastat/pdf_files/GHA_tables.pdf ). Table 1 Size of regions and distribution of districts s/n Region Size (km²) Percentage (%) No. of Districts (1990) Average size of District (km²) 1 Northern 70,384 72.04 13 5,414.15 2 Upper East 8,842 9.05 6 1,473.67 3 Upper West 18,476 18.91 5 3,695.20 Total 97,702 100 24 2.2 Description of data sets National crop output for Sorghum and Millet for the period 1960 to 2015 was obtained from two sources namely the Food and Agriculture Organisation (FAO) ( http://www.fao.org/faostat/en/ ) and the Statistics, Research and Information Directorate (SRID) of the Ministry of Food and Agriculture (MOFA). Datasets from the FAO were aggregated national datasets primarily obtained from the SRID of MOFA and further processed to generate estimates and projections where gaps exist. These datasets included national agricultural output in terms of Production, Yield, and Area harvested for the above-mentioned period. District level datasets obtained from SRID was aggregated into the 110 districts as at the year 1992 in other to ensure consistency and also avoid the ambiguity that comes analyzing datasets with the ever-changing alteration of boundaries (size of districts). The year 1992 also marks the watershed period when Ghana began to collect and publish disaggregated crop data at the district level, making the start year different from that of the national, which is the year 1960. Table 2 above, presents a summary of agricultural datasets use for the analysis. Table 2 Summary of data sourced from FAO and SRID of MOFA S/N Indicator Measurement Source Remarks 1 Production Tonnes SRID/FAO National, Regional, District level 2 Yield Hg/ha SRID/FAO National, Regional, District level 3 Area harvested Hectares (ha) SRID/FAO National, Regional, District level 2.3 Statistical appraisal of levels of productivity 2.3.1 National trends and growth of Sorghum and Millet The growth in Sorghum and Millet determine from assessing the national level output data obtained from the FAO (Table 3). This analysis involves an initial consideration of the basic statistics of crop productivity (Production, Yield and Area Harvested) between the period 1961–2015 including the Mean (m), Maximum, Minimum, Range, Standard Deviation (StDev), Variance and the Coefficient of Variation (CoefVar). The Production is the estimated output measured in metric tonnes (mt), whilst the Area Harvested is the total cropped Area or land-take dedicated to the measured output level in hectares (ha). The Yield, which is a crude measurement of efficiency combines the Production and Area Harvested to determine output per unit land (Hg/ha). The linear trend for crops is assessed to see the levels, peaks, dips and the inter-annual variation. Lastly, a component analysis considered to observe changes in the pattern by comparing the original data to the detrended and a seasonal data using a multiplicative model that shows the Seasonal Indices, per cent variation by season, detrended data by season and residuals by season. 2.3.2 Vector AutoRegression (VAR) analysis VAR is a stochastic statistical process model used to assess the dynamic relationship between multiple time-series variables. VAR expresses all the variables in the process model as a linear function of its past values and the past values of all other variables in the system. The output is a series of equations that can be estimated simultaneously. The ‘order’ of a VAR model designated as the P-value represents the number of lagged values of each variable that are included in the model. There are multiple lags in a VAR model. A VAR(1) model includes only the first lag, VAR(2) includes up to the second lag etc. VAR model may be used for Impulse Response Analysis (Shocks and Innovations) and Granger Causality Test (Identification of direction of influence of variables) which are estimated using least squares, maximum likelihood or Bayesian methods (Qin, 2011). The general form of the \(\:VAR\left(p\right)\) model is given by where \(\:c\) is an arbitrary constant, and all the roots of the polynomial lies outside of the unit circle or equal to one (Klaus, 2016). 2.3.3 The spatial pattern of district level productivity Geostatistical analysis of district-level productivity using simple kriging interpolation method of the 24 districts for the average and totals for the period and assessing the derived maps using the Nugget, Sill, Range, RMSE, Average Standard Error, reveals the pattern of high and low areas of productivity. Additionally, selected periods (1992, 2004 and 2015) interpolated to observe the spatial trend in the expansion or otherwise of the two crops. 2.3.4 The pattern in Millet and Sorghum using the Mann-Kendall and Sen’s slope estimator The Mann-Kendall (MK) test is a Non-parametric statistical test widely used for determining climatological trends. In the case when the value is positive, indicating increasing trends and vice versa while Linear Regression (RL) method is a parametric approach used to test for linear temporal trends [ 21 ]. The Mann-Kendall Statistic (S) has been used in previous studies [ 22 , 23 ] to study trend and the magnitude of change which is determined from Sen’s slope estimator (Q) [ 24 , 25 ]. It provides basic analysis and test of significance for large data sets having irregular sampling intervals, missing data with no requirements for normal distribution of data [ 26 ]. Mann-Kendall (MK) test is an exploratory statistical assessment of monotonic changes (both upward and downward) of the variable of interest over time. Though the test is non-parametric, it can be used as a replacement for parametric linear regression analysis which can be used to show if the slope of the linear regression is different from zero. A parametric test requires that the residual fitted regression line must be a normal distribution, an assumption which is not applicable under a Mann-Kendall test (S) [ 21 , 24 ]. The test aside showing trend and autocorrelation (Sen’s slope) and association between variables (Kendall’s Tau measure) also allows the testing of a null hypothesis (H 0 ) which validates the non-existence of any trend in the series and three (3) other alternatives of: Negative (H a ), No-null (H b ), or Positive trend (H c ). The test is very useful for climatological studies because it takes into account seasonality of the series by considering monthly data and seasonality changes [ 27 , 28 ]. 3.0 Results 3.1 National trends and growth of Sorghum and Millet Sorghum production is at a much higher level of output with a maximum production level of 387,400 metric tonnes in the year 1997 compared to the maximum of Millet which is at 245,550 metric tonnes in the year 2008. The lowest years of production of Millet and Sorghum for the duration of the study is in the years 1964 and 1968 where the output levels are 56,899 and 82,700 metric tonnes, compared to the Mean of 136,285 and 211,266 respectively (Table 3). This higher level of production for Sorghum is revealed in the mean/gradient of the linear trend of growth and the Coefficient of Variation for Millet (136,285/2,033t /32.05) and Sorghum (211,266/4,560t /43.50) respectively (Table 3). The gradient of the trend of Sorghum which is indicative of the growth is more than twice that of Millet (Fig. 2 ). The seasonal analysis which is carried out on almost decadal split of the study period (1961–2015) gives five distinct seasons/ periods, which allows comparison of growth levels. The seasonal analysis reveals a very high increase in output for both crops for the period 1990 to 2010 (season 4) and the lowest season of production from 1970–1980 (season 2) (Fig. 3 ). 3.2 VAR analysis of Yield for Sorghum and Millet Of the 55 observations considered in the study, majority of the data points lie to the right of the mean giving a skewness of 0.86 (Table 4). The plot of millet shows and increasing graph with irregular fluctuations. This show presence of non-stationarity. Stationarity of the process was achieved by taking difference of the logarithm of the process as seen in Fig. 4 (b) below. A formal test of stationarity was conducted to confirm the stationarity of the process. The Augmented Dickey-Fuller Test yield a test statistic of -4.7382 with p-value of 0.01 indicating that the log differenced data is stationarity. The KPSS gave a statistic of 0.063976 and p-value of 0.1 further confirming stationarity of the log difference data Fgure 5 shows that the process reverts to the zero mean, an indication of stationarity. Formal tests were conducted to confirm the stationarity. The Augmented Dickey-Fuller Test gave an empirical statistic of -4.2798 with p-value of 0.01 confirming that the series is stationary whilst the KPSS test gave an empirical statistic of 0.051128 and p-value of 0.1. Both tests confirm that the series is stationary. Since stationarity has been achieved, the length of the lag was then determined and the results is presented in Table 5 below. Table 5: Test for Length of Lag Clearly all the selection criteria except FPE have minimum values under lag 1 indicating that the optimum lag for the process is lag 1. The VAR estimation results for the endogenous variables: Millet, Sorghum has a constant deterministic variable for a sample size of 54 and log likelihood of 306.878. The roots of the characteristic polynomial are 0.8272 and 0.4533. The results for Millet equation: Millet = Millet.l1 + Sorghum.l1 + const are stated below The residual standard error is 0.01608 on 51 degrees of freedom. The equation is significant with \(\:F=51.32\) and \(\:p-value=6.13e-13.\:\text{T}\text{h}\text{e}\:\text{a}\text{d}\text{j}\text{u}\text{s}\text{t}\text{e}\text{d}\:\text{R}-\text{S}\text{q}\text{u}\text{a}\text{r}\text{e}\text{d}\) of 0.655 imply that about 65% of the total variation in yield of millet is accounted for by the explanatory variables in the model. The estimation results for equation Sorghum: Sorghum = Millet.l1 + Sorghum.l1 + const The residual standard error is 0.01753 on 51 degrees of freedom. The models is significant with \(\:F=43.54\:and\:p=9.32e-12.\) The model has an adjusted R-squared of 61.62%, meaning about 62% of variation in yield of sorghum is accounted for the model with only about 38% due to unexplained variation. Table 6 Covariance and Correlation Matrices of Cereals Millet Sorghum Covariance matrix of residuals : Millet 0.0002587 0.0001869 Sorghum 0.0001869 0.0003071 Correlation matrix of residuals : Millet 1.000 0.663 Sorghum 0.663 1.000 The results revealed that both Millet and Sorghum are independent and hence none is important in predicting the other. But the first lag is significant in predicting the current value of each cereal. That notwithstanding, the model explains about 66% and 62% in variation of yield for millet and sorghum respectively. The results further revealed that, increase in yield for millet is linked to an increase in yield for sorghum and vice versa with a correlation coefficient of 0.663 (Table 6 ). A ten-year forecast is then obtained as follows (Table 7) : Table 7: Forecast for millet production The forecast values as seen in Table 7 above are all within the confidence bounds implying that the forecast values are significant with short confidence intervals. This means the estimates are obtained with high statistical power. The forecast however revealed a declining pattern as seen in the Fig. 6 for the ten-year forecast. Pragmatic measures coupled with suitable policies must be instituted to curtail this trend and improve on millet production in the near future. Similarly, the forecast for sorghum is also declining as seen in Table 8 above and a pictorial appraisal in Fig. 7 . The decline is however better than that for millet but needs an intervention to improve sorghum production given that it is involved heavily in the production of most stable foods and local industrial products in Ghana. 3.3 Spatial Pattern Analysis The general spatial pattern of high millet and sorghum output and Yield vary over the entire Northern Savanah Agro-climatic Zone but with higher values recorded for the higher latitudes. The interpolated spatial maps of the average and total production for the two crops reveal that latitudinal increase is associated with higher levels of output with the Upper East and West regions having much of the concentration. Notably, the North-Eastern part of the Upper East region around the Bawku East, Bawku West and the Garu-Tempane districts are the areas with the highest levels of output. The concentrated high levels of production found between latitudes 10° and 11°N with which are the areas much drier with the Guinea Savannah Agro ecology (Figs. 8 and 9 ). Much of the regions from latitudes 8.30° and 10°N that bothers the Forest transition Zone has much less concentrated output. Table 9 summarises, the spatial difference millet and sorghum production. Table 9 Summary of Geostatistical measures Nugget Major Range Partial Sill Root-Mean-Square Average Standard Error Average Millet Production 0.37 1.43 0.80 3,130.54 3,541.33 Average Sorghum Production 0.41 1.48 0.63 7,243.53 6,037.59 Total Millet Production 0.43 1.46 0.70 74,957.64 86,379.76 Total Sorghum Production 0.35 1.46 0.59 170,994.27 150,355.95 1992 Millet 0.00 1.09 1.18 5,966.02 5,824.86 2004 Millet 0.33 1.42 0.58 4626.43 3,779.03 2015 Millet 0.39 0.81 0.71 3112.19 3,493.64 1992 Sorghum 0.95 1.63 0.50 9,444.52 11,349.72 2004 Sorghum 0.40 0.77 0.63 12,478.97 12,267.25 2015 Sorghum 0.62 1.53 0.23 6,151.30 5,455.97 3.4 The pattern in Millet and Sorghum using the Mann-Kendall and Sen’s slope estimator The non-parametric Mann-Kendall test to find the presence of a monotonic increasing and decreasing trend resulted in a null S test output because the number of annual values for the test is more than 10 (55 years). On the other hand, the Z test (normal approximation) gives a positive figure (Table 10 ) which shows an upward increase for all measurement of Production, Yield and Area Harvested and ranges between 5.86 and 6.20 at a significance level of 0.001 (0.1%). The only exception to the above is Area harvested for Millet which is 0.83, in all there is a positive, upward trend. Sen’s slope estimator (Q) a linear model determines the slope of the trend and the variance of the residuals, which is constant over time. The Q estimator for production for Sorghum (4,509.38) is more than twice that for Millet (2,046.50), however, the Q measure for Area Harvested reveals a converse relationship between production and the land dedicated to it as Millet (277.78) has a much higher trend slope than Sorghum (87.64) (Fig. 10 ). Table 10 Mann-Kendall and Sen’s slope estimator results for Millet and Sorghum (1961–2015) Measure PRODUCTION YIELD AREA HARVESTED Crop Millet Sorghum Millet Sorghum Millet Sorghum N 55 55 55 55 55 55 Test S *** *** *** *** *** *** Test Z 5.86 5.92 6.20 6.14 0.83 6.20 Signific. *** *** *** *** *** *** Q 2,046.50 4,509.38 87.64 105.63 277.78 87.64 Qmin99 1,378.54 3,027.79 55.70 73.45 -779.51 55.70 Qmax99 2,736.23 5,724.59 113.21 134.99 1,256.76 113.21 Qmin95 1,561.51 3,499.83 63.24 80.57 -465.31 63.24 Qmax95 2,573.14 5,380.35 104.85 129.18 1,076.34 104.85 B 78,070.00 99,851.25 4,908.04 5,795.89 172,777.78 4,908.04 Bmin99 93,872.48 125,565.63 5,752.42 6,656.62 214,795.89 5,752.42 Bmax99 62,686.97 57,060.79 4,314.15 5,040.63 143,446.00 4,314.15 Bmin95 89,961.94 112,077.35 5,570.79 6,454.97 201,941.54 5,570.79 Bmax95 67,214.86 68,264.75 4,461.62 5,162.81 149,541.93 4,461.62 ***Significance level = The Qmin99 [(Table 5: row 5 column 6): the lower limit of the 99% confidence interval of Q (α = 0.1) = -779.51] and the Qmin95 [(Table 5: row 7 column 6): the lower limit of the 95% confidence interval of Q (α = 0.05) = -465.31] for Area Harvested for Millet are both negative and very dissimilar as compared to the other productivity measures and for even Area Harvested for Sorghum (Table 5). 4.0 Discussion and conclusions The country produces just about 51% of its cereal needs [ 29 ] and about half of this imported needs are imported rice and maize. However, Millet and Sorghum do not fall into this category of imported cereals as demand is wholly met by local production. The Periods of low productivity of the two crops, where the rate of change departs significantly from the general trend of increasing production level, can be attributed to climate extreme events such as droughts and its accompanying wildfires. The drought of the period 1981–1983 is associated with a decline in output for the two crops of Millet and Sorghum and similar decline in output for rice and maize. Similarly, the years 1979, 1989 and 2007 also reveal such decline in output generally for agriculture [ 10 ] and for Millet and Sorghum. This is as a result of decreasing monthly and annual rainfall amount which results in water deficits and agricultural drought that affects crop growth [ 30 ] and bushfires that destroys large hectares of farmlands [ 31 ]. Though a case can be made for the favourable weather and climatic conditions as the driving factor for the years of bumper harvest recognisable by distinct peaks in the time series plots, other policy interventions are equally very important factors in achieving such milestones. It is observed that, there are periods where the increased outputs can directly be attributed directly to a particular government policy. In the Northern Savannah Agro-climatic Zone, notable policy interventions during this period which have contributed to increased productivity include the Land Conservation and Smallholder Rehabilitation Project (LACOSREP) I & II, Ghana Agricultural Sector Investment Programme (GASIP) -- (June 2015 - June 2019), Northern Rural Growth Productivity Programme (NRGP), West African Agricultural Productivity Programme (WAAPP). These programme interventions included farmer capacity development, provision of enhanced extension services, subsidised cost of inputs, access to cheaper equipment and machinery etc. [ 32 ]. The increasing trend in output for Millet and Sorghum has been accompanied by a contemporaneous increase in farm sizes which calls into question an investigation into the extent to which land as an input factor and other socio-economic and environmental factors have contributed to production levels. There is therefore less appreciable inter-annual increase in Yield. There is a general upward trend generally for agricultural productivity, and these increases save some years where there was a comparative decline in output is positive and accompanied by a commensurate level of increases for cropped area / Area harvested. This makes it easy to conclude that the gains are as a result of the land take increase. However, such a conclusion would not be empirically justifiable because it does not consider the contribution of other inputs such as labour, enterprise, capital and agro-inputs like fertilizer and implements and how economic policy and implementation targeted programmes affects all other inputs and farmer motivation. We do recommend that further work that considers the efficiency of the production inputs be explored to ascertain why increases in output is accompanied by increasing cropped Area denoting agricultural expansion with little regard for intensification. The following specific attributions are made concerning the analysis of the growth of Millet and Sorghum. Sorghum production has always been higher than Millet production for the entire duration and the rates on the increase in production have always been higher, increasing much more rapidly from the period 1987 and almost doubling the output rate for Millet. The drier regions in the Upper East region and West regions have a much greater output than the wetter areas close to the Forest transitional Zones, and the increasing latitudinal increase corresponds to higher levels of production for both crops. The central portions of the Northern region have very sparse production levels owing basically the large sizes of the districts and the existence of forest reserves in the areas that cover the East Gonja, West Gonja and Central Gonja districts. The Mann-Kendall test (z), apart from the yield levels where Millet has the highest upward trend than Sorghum, Sorghum has significantly higher test Z than Millet in the Production and Area Harvested. This means that Millet is cultivated much more intensively than Sorghum and much of the increases in the production of Sorghum is attributable to a commensurate increase in the Area Harvested. Declarations Author Contribution 1 - Conceived and designed the experiments; S. A. 2 - Performed the experiments; S. A., J. N. A. A., D. J.3 - Analyzed and interpreted the data; S. A., J. N. A. A., D. J. andT. A.4 - Contributed reagents, materials, analysis tools or data; S. A., J. N. A. A., D. J. T. A and L. K. 5 - Wrote the paper; S. A.6 - Review of manuscript; S. A., J. N. A. A., D. J. T. A and L. K. Data Availability SOURCES OF DATA USED FOR THIS RESEARCH HAVE BEEN EXPLICITLY STATED IN THE MANUSCRIPT. THESE ARE PUBLIC DATASETS PROVIDED FROM REPOSITORIES ONLINE. References Dourado-Neto, D., Teruel, D. A., Reichardt, K., Nielsen, D. R., Frizzone, J. A., & Bacchi, O. O. S. (1998). Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science. Scientia Agricola , 55 , 46–50. https://doi.org/http://dx.doi.org/10.1590/S0103-90161998000500008 Moeletsi, M. E., & Walker, S. (2012). A simple agroclimatic index to delineate suitable growing areas for rainfed maize production in the Free State Province of South Africa. 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Agriculture, Ecosystems & Environment , 125 (1-4), 33–47. https://doi.org/10.1016/j.agee.2007.10.008 Mensah, C., Amekudzi, L. K., Klutse, N. A. B., Aryee, J. N. A., & Asare, K. (2016). Comparison of Rainy Season Onset , Cessation and Duration for Ghana from RegCM4 and GMet Comparison of Rainy Season Onset , Cessation and Duration for Ghana from RegCM4 and GMet Datasets. Atmospheric and Climate Sciences , 6 (April), 300–309. https://doi.org/10.4236/acs.2016.62025 Friesen, J.; Stomph, T.; de Ridder, N.; Steenhuis, T.; van de Giesen, N.; Reudenbach, C.; Bendix, J.;Winiger, M.; Berger, T.; Park, S. . et al. (2002). Spatio-temporal patterns of rainfall in Northern Ghana. Earth , 27 (8). Larbi, I., Hountondji, F., Annor, T., Agyare, W., Mwangi Gathenya, J., & Amuzu, J. (2018). Spatio-Temporal Trend Analysis of Rainfall and Temperature Extremes in the Vea Catchment, Ghana. Climate , 6 (4), 87. https://doi.org/10.3390/cli6040087 Ndamani, F., & Watanabe, T. (2014). Rainfall variability and crop production in northern Ghana : The case of Lawra district. Society for Social Management Systems Internet J Ournal . https://doi.org/10.13140/2.1.2343.7125 UNEP, & UNDP. (2013). National Climate Change Adaptation Strategy. In Global Environmental Change (Vol. 5). https://doi.org/10.1007/978-3-319-31499-0 Quaye, W., Yawson, R., Ayeh, E., & Yawson, I. (2012). Climate change and food security: The role of biotechnology. African Journal of Food, Agriculture, Nutrition and Development , 12 (5), 6354–6364. Retrieved from http://www.ajfand.net/Volume12/No5/Quaye11120.pdf Asante, F., & Amuakwa-Mensah, F. (2014). Climate Change and Variability in Ghana: Stocktaking. Climate , 3 (1), 78–99. https://doi.org/10.3390/cli3010078 Wood, T. N. (2013). Agricultural Development in the Northern Savannah of Ghana (University of Nebraska-Lincoln). Retrieved from http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1000&context=planthealthdoc Tan, C. M., & Rockmore, M. (2017). Famine in Ghana and Its Impact. In P. V. Preedy V. (Ed.), Handbook of Famine, Starvation, and Nutrient . https://doi.org/https://doi.org/10.1007/978-3-319-40007-5_95-1 Rademacher-Schulz, C., Schraven, B., & Mahama, E. S. (2014). Time matters: Shifting seasonal migration in Northern Ghana in response to rainfall variability and food insecurity. Climate and Development , 6 (1), 46–52. https://doi.org/10.1080/17565529.2013.830955 Awine, T., Azongo, D. K., Binka, F. N., Rexford Oduro, A., & Wak, G. (2012). A time series analysis of weather variables and all-cause mortality in the Kasena-Nankana Districts of Northern Ghana, 1995–2010. Global Health Action , 5 (1), 19073. https://doi.org/10.3402/gha.v5i0.19073 Wood, S., Guo, Z., & Wood-Sichra, U. (2016). Spatial Patterns of Agricultural Productivity. In S. Benin (Ed.), Agricultural Productivity in Africa: Trends, Patterns and Determinants (pp. 105–132). https://doi.org/http://dx.doi.org/10.2499/9780896298811 Smith, R. H., & Bhaskaran, S. (1986). Sorghum [Sorghum bicolor (L.) Moench]. In Part of the Biotechnology in Agriculture and Forestry (pp. 220–233). https://doi.org/https://doi.org/10.1007/978-3-642-61625-9_13 Reddy, P. S. (2017). Pearl Millet, Pennisetum glaucum (L.) R. Br. In J. V. Patil (Ed.), Millets and Sorghum: Biology and Genetic Improvement (pp. 49–86). https://doi.org/https://doi.org/DOI:10.1002 Aryee, J. N. A. et al. (2018). Development of High Spatial Resolution Rainfall Data for Ghana. International Journal of Climatology , 38 (6), 1201–1215. https://doi.org/doi.org/10.1002/joc.5238 Safari, B. (2012). Trend Analysis of the Mean Annual Temperature in Rwanda during the Last Fifty Two Years. Journal of Environmental Protection , 03 (06), 538–551. https://doi.org/10.4236/jep.2012.36065 Opiyo, F., Nyangito, M., Wasonga, V. O., & Omondi, P. (2014). Trend analysis of rainfall and temperature variability in arid environment in Turkana, Kenya. Environmental Research Journal , 8 (2), 30–43. Ateeq-ur-Rauf, Ahmad, J., Khan, N., Ahmed, S., & Asadullah. (2016). Precipitation Trend Analysis of Upper Indus Basin Using Mann Kendall Method. 2nd International Multi-Disciplinary Conference Gujrat, Pakistan. The University of Lahore. , (December). Gujrat. Singh, D., Jain, S., Gupta, R., Kumar, S., Rai, S., & Jain, N. (2016). Analyses of Observed and Anticipated Changes in Extreme Climate Events in the Northwest Himalaya. Climate , 4 (1), 9. https://doi.org/10.3390/cli4010009 Rahman, M. A., Yunsheng, L., & Sultana, N. (2016). Analysis and prediction of rainfall trends over Bangladesh using Mann–Kendall, Spearman’s rho tests and ARIMA model. Meteorol Ogy and Atmospheric Physics , 128 , 1–16. https://doi.org/doi:10.1007/s00703-016-0479-4 Kabo-Bah, A., Diji, C., Nokoe, K., Mulugetta, Y., Obeng-Ofori, D., & Akpoti, K. (2016). Multiyear Rainfall and Temperature Trends in the Volta River Basin and their Potential Impact on Hydropower Generation in Ghana. Climate , 4 (4), 49. https://doi.org/10.3390/cli4040049 Pohlert, T. (2016). Package “ trend ”: Non-Parametric Trend Tests and Change-Point Detection (p. 26). p. 26. Retrieved from http://creativecommons.org/licenses/by-nd/4.0/ Bari, S. H., Rahman, M. T. U., Hoque, M. A., & Hussain, M. M. (2016). Analysis of seasonal and annual rainfall trends in the northern region of Bangladesh. Atmospheric Research , 176-177 (July), 148–158. https://doi.org/10.1016/j.atmosres.2016.02.008 Darfour, B., & Rosentrater, K. A. (2016). Agriculture and Food Security in Ghana. 2016 Agricultural and Biosystems EngineeringConference Proceedings and Presentations Annual International Meeting , 1–11. https://doi.org/doi:10.13031/aim.20162460507 Brown, M., Black, E., Asfaw, D., & Otu-larbi, F. (2017). Monitoring drought in Ghana using TAMSAT-ALERT : a new decision support system. Weather , 72 (7), 201–205. https://doi.org/doi:10.1002/wea.3033 Armah, F. A., Yawson, D. O., Yengoh, G. T., Odoi, J. O., & Afrifa, E. K. a. (2010). Impact of Floods on Livelihoods and Vulnerability of Natural Resource Dependent Communities in Northern Ghana. Water , 2 (2), 120–139. https://doi.org/10.3390/w2020120 Asuming-Brempong, S., & K. M. Kuwornu, J. (2013). Policy Initiatives and Agricultural Performance in Post-Independent Ghana. Journal of Social and Development Sciences , 4 (9), 425–434. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5508540","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416650796,"identity":"e1becd88-a391-4643-b3bc-83505b4aa12e","order_by":0,"name":"Steve Ampofo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYNACGwY5Bh44L4GBgbGBkJY0BmMGHmYGhgOkaElsIFqLfPvptAcfEmzS+3vOH5P+UFPHwM+eY8D4dQduLQZncrcbzkhIy51xtplN4sCxwwySPW8MmGXP4NHCkLtNmvfH4dwN/MxALWwHGAxu5BgwS7bhcVj/223SPAmH0w3AWv7VMdgT0sJwIxesJcGAF+iwg23MDAYSQL98xKPF4MZbsF8MZ5w5bGxxtu8wj8SZZwWHGfE6LHcbKMTk+XsSH96o+FYnx9+evPHhT3wOY2BgQ+GBE8FhHqwqcWgBA8Yf+LWMglEwCkbByAIA/L1Se51tVDUAAAAASUVORK5CYII=","orcid":"","institution":"C. K. Tedam University of Technology and Applied Sciences (CKT-UTAS)","correspondingAuthor":true,"prefix":"","firstName":"Steve","middleName":"","lastName":"Ampofo","suffix":""},{"id":416650797,"identity":"13dfa108-cc15-4579-b92c-1eabf880743d","order_by":1,"name":"Jeffrey N. A. Aryee","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jeffrey","middleName":"N. A.","lastName":"Aryee","suffix":""},{"id":416650798,"identity":"5bd09576-64a9-477f-b8c6-ae8007538042","order_by":2,"name":"Thompson Annor","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Thompson","middleName":"","lastName":"Annor","suffix":""},{"id":416650799,"identity":"92901f2a-3588-4a45-b3f5-504c3dd3c40d","order_by":3,"name":"Leonard K. Amekudzi","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Leonard","middleName":"K.","lastName":"Amekudzi","suffix":""},{"id":416650800,"identity":"1b081fdf-d07b-4b6b-bc40-13958d6e82a4","order_by":4,"name":"Dioggban Jakperik","email":"","orcid":"","institution":"C. K. Tedam University of Technology and Applied Sciences (CKT-UTAS)","correspondingAuthor":false,"prefix":"","firstName":"Dioggban","middleName":"","lastName":"Jakperik","suffix":""}],"badges":[],"createdAt":"2024-11-23 07:23:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5508540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5508540/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76630496,"identity":"c4105dd6-5751-4f26-a54b-8bb631ef621f","added_by":"auto","created_at":"2025-02-19 06:38:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":186237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap showing Study area and the districts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/dca514426747fdbb472e144c.png"},{"id":76630487,"identity":"80deba4d-da18-42b8-9fe7-4b609d857d99","added_by":"auto","created_at":"2025-02-19 06:38:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65250,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrend analysis for Sorghum (left) and Millet (Right) production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/d504afb62ff40754ba606a7c.png"},{"id":76630199,"identity":"b37e4746-66f5-4cb7-8c86-c85b244d0d98","added_by":"auto","created_at":"2025-02-19 06:30:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104943,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComponent and seasonal Analysis for Millet (top) and Sorghum (down)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/8ea77582e2ae16f93b75a994.png"},{"id":76630193,"identity":"5db2dad8-3f4f-4f18-b6c7-4410a044ec99","added_by":"auto","created_at":"2025-02-19 06:30:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24866,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of Raw Millet Data (left) and Plot of the log differenced Millet data (right)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/85fbda317b141ac29b9b2386.png"},{"id":76630485,"identity":"7bd60129-58d5-4e71-8eef-43ea56138377","added_by":"auto","created_at":"2025-02-19 06:38:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePlot of Raw Data for Sorghum (left) and Plot of log difference data for Sorghum (right)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/6474ed1da5748e1d2c7c1913.png"},{"id":76630221,"identity":"4f5a6de0-3ab9-429c-bf64-88928af1442a","added_by":"auto","created_at":"2025-02-19 06:30:07","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":95850,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForecast series of millet\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/94fb197b821949de00730c2b.png"},{"id":76630210,"identity":"6148dc94-8cc9-40a4-9ee2-e56f27778d65","added_by":"auto","created_at":"2025-02-19 06:30:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":97538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForecast series of sorghum\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/c426e10873adb44e29ab9139.png"},{"id":76630202,"identity":"84c90cab-72fd-445d-ab67-180aad5e7efc","added_by":"auto","created_at":"2025-02-19 06:30:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":97054,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterpolation maps for Total Millet (left) and Sorghum (right) Production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/f33264cc9121453be8a24e83.png"},{"id":76630226,"identity":"68dc1732-0024-4307-abad-6f744757b693","added_by":"auto","created_at":"2025-02-19 06:30:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":99536,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterpolation maps for Average Millet (left) and Sorghum (right) Production\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/70db7c68aefab4530d746173.png"},{"id":76630229,"identity":"898836b4-fc15-4b0b-8319-21b2143eed12","added_by":"auto","created_at":"2025-02-19 06:30:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":129710,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResidual and Sen’s slope estimate for Millet (Left) and Sorghum (Right) Production (Top), Area Harvested (Middle) and Yield (Bottom)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/e48b72742ea92fc9fef3fd5d.png"},{"id":76633216,"identity":"65e80b7f-0477-41d0-aada-3f1c5df640fb","added_by":"auto","created_at":"2025-02-19 07:02:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2359252,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5508540/v1/16b86dc4-0b5a-4b03-aa0a-888dc0615d40.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends and Spatial Patterns of Sorghum and Millet Production in the Northern Savannah Agro-climatic Zone of Ghana; A statistical Appraisal","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThere is substantial evidence to show the effects of climate change on terrestrial food production in several regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. There are challenges to ensure continued increases in agricultural output to meet growing food demands globally as a result of the potential impact of climate change and variability [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and this is also true for much of West Africa (Roudier et al., 2011). Regions in high latitudes have experienced increases in food production whiles those in the low latitudes have experienced decreases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These studies on climate change are mostly at the regional scale and have often not considered local needs, output and variation, but on crops that have international significance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSorghum and Millet are the two most essential cereals among many others grown in the Northern Savannah Climatic Zone (NSAZ) as the Agro-climatic zone have climatic and ecological conditions which are well suited to these crops. The location described as the food basket of Ghana is the leading supplier of cereals. Sorghum and Millet are essential staples in the diet of indigenes of the three regions in the zone and are used in most traditional meals. However, climate change and variability pose a severe threat to the productivity of these crops, because there is an increasing trend of farmers who though prefer Sorghum and Millet are now shifting to the cultivation of other crops such as maize (Zea Mays) and sweet potato [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe climate of the northern Savannah Agro-climatic Zone has undergone some variation which affects farmer\u0026rsquo;s ability to forecast and also expectations to engage in rain-fed agriculture [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The period of inadequate food provisioning defined as the number of months between stock depletion and the next harvest for Sorghum and Millet for the three regions of the zone is at least five months [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] with severe socio-economic implications creating food insecurity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and concomitant effects of seasonal migration [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and health [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The response to the impact of climate variability and change on agriculture has been the expansion of farmlands into marginal Savannah lands and forest zones, and this phenomenon is only constrained in extent by the spatial heterogeneity in the agroecology, cultural and socio-economic conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSorghum \u003cem\u003e(Sorghum Bicolor (L.) Moench)\u003c/em\u003e is of tropical origin also referred to variously as Durra, Jowari, or Milo but can grow in both temperate and tropical zones. Sorghum is a grass specie grown as food for humans, animal feed, and also for ethanol production. It originated from Africa and is the world\u0026rsquo;s fifth-largest cultivated cereal after rice, wheat, maize and barley grown mostly in tropical and sub-tropical regions. It is an annual crop that grows in clumps that may reach over 4m high at maturity. It has high soil fertility requirements and increased drought tolerance with a minimum root depth of 12 inches thriving on many marginal sites. Its unique physiology makes it one of the toughest of all cereals. Sorghum is one of the quickest maturing food plants (certain types can grow in as little as 75 days and can provide three harvests a year). Grain size is small and ranges from 2 to 4 mm in diameter, and it is hardly different from wheat and maize in nutrients composition which is 70% carbohydrate, 12 per cent protein, 3%t fat, 2% fibre, and 1.5% ash. Sorghum has a minimum temperature requirement of 8.33 (\u0026deg;C) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePearl Millet (\u003cem\u003ePennisetum glaucum (L.) R.Br\u003c/em\u003e.) is a food crop grown widely in Africa and India. It is used for a variety of food products and local beer. It has early maturation and highly valued by farmers because of its efficient use of water, adaptation to very arid conditions and nutrient-poor sandy soils, drought-resistant, less tolerant of waterlogging and flooding compared to Sorghum. This leafy grass plant also called cattail, bulrush and candle Millet has a sowing window between the months of May to September. Germination and emergence of the plant occur five days after planting and proliferates within 14 days before reaching the next phase of flowering within 60 to 70 days from growing. In northern Ghana, although the rainfall is unimodal, it is possible to have two growing cycle for Millet due to its early maturation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe challenge of ensuring food security for all the indigenes of the NSAZ, and reinforcing the notion of the NSAZ as the food basket for the entire country particularly with regards to the supply of grains and cereals makes it imperative to investigate agricultural productivity levels for Sorghum and Millet. This task is even more relevant considering that it covers about a third of the size of Ghana yet has a unimodal rainfall regime which makes it prone the impacts of climate variability and change. This paper seeks to assess the effects of climate change and climate variability on the production of Millet and Sorghum. Additionally, this paper identifies and map out the areas of high concentration of production and relate the spatial output levels to the latitudinal change.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study area\u003c/h2\u003e\n \u003cp\u003eGhana is located in Southern coast of West Africa bounded to the east by Togo, the west by La Cote D\u0026rsquo;ivoire, the north by Burkina Faso and south by the Atlantic Ocean referred to as the Gulf of Guinea. It lies between latitudes 4\u0026deg; 44\u0026rsquo; N and 11\u0026deg; 11\u0026rsquo;N and longitudes 3\u0026deg; 11\u0026rsquo; W and 1\u0026deg; 11\u0026rsquo; E. It is composed of four (4) distinct Agro-climatic zones (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which differ in terms of the climatic variables of seasons, temperature, rainfall amount, and rainy season characteristics [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe Northern Savannah Agro-climatic Zone is the largest of the four zones with an area of approximately 87,584 km\u0026sup2; in size covering the entirety of the three (3) administrative regions of Upper East, Upper West and the erstwhile Northern region (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), which is now decomposed into the Savannah, North-East and Northern regions. It is composed of the Guinea Savannah and Sudan savannah vegetation types. Guinea and the Sudan savannah vegetation in this Agro-climatic zone cover 37% of the total landmass of Ghana and have an average annual rainfall of 1050 mm per year. Observed differences between these two ecological formations are the length of the growing season which is 180\u0026ndash;200 days for the Guinea savannah and 150\u0026ndash;160 days for the Sudan savannah (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/fileadmin/user_upload/aquastat/pdf_files/GHA_tables.pdf\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSize of regions and distribution of districts\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003es/n\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSize (km\u0026sup2;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNo. of Districts (1990)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage size of District (km\u0026sup2;)\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\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNorthern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70,384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,414.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper East\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,473.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUpper West\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18,476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,695.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97,702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Description of data sets\u003c/h2\u003e\n \u003cp\u003eNational crop output for Sorghum and Millet for the period 1960 to 2015 was obtained from two sources namely the Food and Agriculture Organisation (FAO) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fao.org/faostat/en/\u003c/span\u003e\u003c/span\u003e) and the Statistics, Research and Information Directorate (SRID) of the Ministry of Food and Agriculture (MOFA). Datasets from the FAO were aggregated national datasets primarily obtained from the SRID of MOFA and further processed to generate estimates and projections where gaps exist. These datasets included national agricultural output in terms of Production, Yield, and Area harvested for the above-mentioned period. District level datasets obtained from SRID was aggregated into the 110 districts as at the year 1992 in other to ensure consistency and also avoid the ambiguity that comes analyzing datasets with the ever-changing alteration of boundaries (size of districts). The year 1992 also marks the watershed period when Ghana began to collect and publish disaggregated crop data at the district level, making the start year different from that of the national, which is the year 1960. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e above, presents a summary of agricultural datasets use for the analysis.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of data sourced from FAO and SRID of MOFA\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIndicator\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasurement\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRemarks\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\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProduction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTonnes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRID/FAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational, Regional, District level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHg/ha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRID/FAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational, Regional, District level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArea harvested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHectares (ha)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSRID/FAO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNational, Regional, District level\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Statistical appraisal of levels of productivity\u003c/h2\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 National trends and growth of Sorghum and Millet\u003c/h2\u003e\n \u003cp\u003eThe growth in Sorghum and Millet determine from assessing the national level output data obtained from the FAO (Table\u0026nbsp;3). This analysis involves an initial consideration of the basic statistics of crop productivity (Production, Yield and Area Harvested) between the period 1961\u0026ndash;2015 including the Mean (m), Maximum, Minimum, Range, Standard Deviation (StDev), Variance and the Coefficient of Variation (CoefVar). The Production is the estimated output measured in metric tonnes (mt), whilst the Area Harvested is the total cropped Area or land-take dedicated to the measured output level in hectares (ha). The Yield, which is a crude measurement of efficiency combines the Production and Area Harvested to determine output per unit land (Hg/ha). The linear trend for crops is assessed to see the levels, peaks, dips and the inter-annual variation. Lastly, a component analysis considered to observe changes in the pattern by comparing the original data to the detrended and a seasonal data using a multiplicative model that shows the Seasonal Indices, per cent variation by season, detrended data by season and residuals by season.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 Vector AutoRegression (VAR) analysis\u003c/h2\u003e\n \u003cp\u003eVAR is a stochastic statistical process model used to assess the dynamic relationship between multiple time-series variables. VAR expresses all the variables in the process model as a linear function of its past values and the past values of all other variables in the system. The output is a series of equations that can be estimated simultaneously. The \u0026lsquo;order\u0026rsquo; of a VAR model designated as the P-value represents the number of lagged values of each variable that are included in the model. There are multiple lags in a VAR model. A VAR(1) model includes only the first lag, VAR(2) includes up to the second lag etc. VAR model may be used for Impulse Response Analysis (Shocks and Innovations) and Granger Causality Test (Identification of direction of influence of variables) which are estimated using least squares, maximum likelihood or Bayesian methods (Qin, 2011). The general form of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VAR\\left(p\\right)\\)\u003c/span\u003e\u003c/span\u003e model is given by\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"555\" height=\"52\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:c\\)\u003c/span\u003e\u003c/span\u003e is an arbitrary constant, and all the roots of the polynomial lies outside of the unit circle or equal to one (Klaus, 2016).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.3 The spatial pattern of district level productivity\u003c/h2\u003e\n \u003cp\u003eGeostatistical analysis of district-level productivity using simple kriging interpolation method of the 24 districts for the average and totals for the period and assessing the derived maps using the Nugget, Sill, Range, RMSE, Average Standard Error, reveals the pattern of high and low areas of productivity. Additionally, selected periods (1992, 2004 and 2015) interpolated to observe the spatial trend in the expansion or otherwise of the two crops.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.4 The pattern in Millet and Sorghum using the Mann-Kendall and Sen\u0026rsquo;s slope estimator\u003c/h2\u003e\n \u003cp\u003eThe Mann-Kendall (MK) test is a Non-parametric statistical test widely used for determining climatological trends. In the case when the value is positive, indicating increasing trends and vice versa while Linear Regression (RL) method is a parametric approach used to test for linear temporal trends [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The Mann-Kendall Statistic (S) has been used in previous studies [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] to study trend and the magnitude of change which is determined from Sen\u0026rsquo;s slope estimator (Q) [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. It provides basic analysis and test of significance for large data sets having irregular sampling intervals, missing data with no requirements for normal distribution of data [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eMann-Kendall (MK) test is an exploratory statistical assessment of monotonic changes (both upward and downward) of the variable of interest over time. Though the test is non-parametric, it can be used as a replacement for parametric linear regression analysis which can be used to show if the slope of the linear regression is different from zero. A parametric test requires that the residual fitted regression line must be a normal distribution, an assumption which is not applicable under a Mann-Kendall test (S) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The test aside showing trend and autocorrelation (Sen\u0026rsquo;s slope) and association between variables (Kendall\u0026rsquo;s Tau measure) also allows the testing of a null hypothesis (H\u003csub\u003e0\u003c/sub\u003e) which validates the non-existence of any trend in the series and three (3) other alternatives of: Negative (H\u003csub\u003ea\u003c/sub\u003e), No-null (H\u003csub\u003eb\u003c/sub\u003e), or Positive trend (H\u003csub\u003ec\u003c/sub\u003e). The test is very useful for climatological studies because it takes into account seasonality of the series by considering monthly data and seasonality changes [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 National trends and growth of Sorghum and Millet\u003c/h2\u003e\n \u003cp\u003eSorghum production is at a much higher level of output with a maximum production level of 387,400 metric tonnes in the year 1997 compared to the maximum of Millet which is at 245,550 metric tonnes in the year 2008. The lowest years of production of Millet and Sorghum for the duration of the study is in the years 1964 and 1968 where the output levels are 56,899 and 82,700 metric tonnes, compared to the Mean of 136,285 and 211,266 respectively (Table 3). This higher level of production for Sorghum is revealed in the mean/gradient of the linear trend of growth and the Coefficient of Variation for Millet (136,285/2,033t /32.05) and Sorghum (211,266/4,560t /43.50) respectively (Table 3). The gradient of the trend of Sorghum which is indicative of the growth is more than twice that of Millet (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" height=\"339\" width=\"713\" style=\"width: 713px;\"\u003e\u003c/p\u003e\n \u003cp\u003eThe seasonal analysis which is carried out on almost decadal split of the study period (1961\u0026ndash;2015) gives five distinct seasons/ periods, which allows comparison of growth levels. The seasonal analysis reveals a very high increase in output for both crops for the period 1990 to 2010 (season 4) and the lowest season of production from 1970\u0026ndash;1980 (season 2) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 VAR analysis of Yield for Sorghum and Millet\u003c/h2\u003e\n \u003cp\u003e\u003cimg 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\" width=\"814\" height=\"157\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eOf the 55 observations considered in the study, majority of the data points lie to the right of the mean giving a skewness of 0.86 (Table 4). The plot of millet shows and increasing graph with irregular fluctuations. This show presence of non-stationarity. Stationarity of the process was achieved by taking difference of the logarithm of the process as seen in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (b) below.\u003c/p\u003e\n \u003cp\u003eA formal test of stationarity was conducted to confirm the stationarity of the process. The Augmented Dickey-Fuller Test yield a test statistic of -4.7382 with p-value of 0.01 indicating that the log differenced data is stationarity. The KPSS gave a statistic of 0.063976 and p-value of 0.1 further confirming stationarity of the log difference data\u003c/p\u003e\n \u003cp\u003eFgure 5 shows that the process reverts to the zero mean, an indication of stationarity. Formal tests were conducted to confirm the stationarity. The Augmented Dickey-Fuller Test gave an empirical statistic of -4.2798 with p-value of 0.01 confirming that the series is stationary whilst the KPSS test gave an empirical statistic of 0.051128 and p-value of 0.1. Both tests confirm that the series is stationary. Since stationarity has been achieved, the length of the lag was then determined and the results is presented in Table 5 below.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;5: Test for Length of Lag\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u003cimg 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\" width=\"720\" height=\"216\"\u003e\u003c/h3\u003e\n\u003cp\u003eClearly all the selection criteria except FPE have minimum values under lag 1 indicating that the optimum lag for the process is lag 1.\u003c/p\u003e\n\u003cp\u003eThe VAR estimation results for the endogenous variables: Millet, Sorghum has a constant deterministic variable for a sample size of 54 and log likelihood of 306.878. The roots of the characteristic polynomial are 0.8272 and 0.4533. The results for Millet equation: Millet\u0026thinsp;=\u0026thinsp;Millet.l1\u0026thinsp;+\u0026thinsp;Sorghum.l1\u0026thinsp;+\u0026thinsp;const are stated below\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cimg 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\" height=\"194\" width=\"638\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe residual standard error is 0.01608 on 51 degrees of freedom. The equation is significant with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F=51.32\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p-value=6.13e-13.\\:\\text{T}\\text{h}\\text{e}\\:\\text{a}\\text{d}\\text{j}\\text{u}\\text{s}\\text{t}\\text{e}\\text{d}\\:\\text{R}-\\text{S}\\text{q}\\text{u}\\text{a}\\text{r}\\text{e}\\text{d}\\)\u003c/span\u003e\u003c/span\u003e of 0.655 imply that about 65% of the total variation in yield of millet is accounted for by the explanatory variables in the model.\u003c/p\u003e\n\u003cp\u003eThe estimation results for equation Sorghum:\u003c/p\u003e\n\u003cp\u003eSorghum\u0026thinsp;=\u0026thinsp;Millet.l1\u0026thinsp;+\u0026thinsp;Sorghum.l1\u0026thinsp;+\u0026thinsp;const\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003e\u003cimg 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\" width=\"632\" height=\"193\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eThe residual standard error is 0.01753 on 51 degrees of freedom. The models is significant with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F=43.54\\:and\\:p=9.32e-12.\\)\u003c/span\u003e\u003c/span\u003e The model has an adjusted R-squared of 61.62%, meaning about 62% of variation in yield of sorghum is accounted for the model with only about 38% due to unexplained variation.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCovariance and Correlation Matrices of Cereals\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\"\u003e\n \u003cp\u003eMillet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSorghum\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\u003cstrong\u003eCovariance matrix of residuals\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMillet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0002587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001869\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0001869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0003071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation matrix of residuals\u003c/strong\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMillet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe results revealed that both Millet and Sorghum are independent and hence none is important in predicting the other. But the first lag is significant in predicting the current value of each cereal. That notwithstanding, the model explains about 66% and 62% in variation of yield for millet and sorghum respectively. The results further revealed that, increase in yield for millet is linked to an increase in yield for sorghum and vice versa with a correlation coefficient of 0.663 (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). A ten-year forecast is then obtained as follows (Table\u0026nbsp;7) :\u003c/p\u003e\n\u003cp\u003eTable 7: Forecast for millet production\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"547\" height=\"439\"\u003e\u003c/p\u003e\n\u003cp\u003eThe forecast values as seen in Table 7 above are all within the confidence bounds implying that the forecast values are significant with short confidence intervals. This means the estimates are obtained with high statistical power. The forecast however revealed a declining pattern as seen in the Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e for the ten-year forecast. Pragmatic measures coupled with suitable policies must be instituted to curtail this trend and improve on millet production in the near future.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\" width=\"569\" height=\"471\"\u003e\u003c/p\u003e\n\u003cp\u003eSimilarly, the forecast for sorghum is also declining as seen in Table 8 above and a pictorial appraisal in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. The decline is however better than that for millet but needs an intervention to improve sorghum production given that it is involved heavily in the production of most stable foods and local industrial products in Ghana.\u003c/p\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Spatial Pattern Analysis\u003c/h2\u003e\n \u003cp\u003eThe general spatial pattern of high millet and sorghum output and Yield vary over the entire Northern Savanah Agro-climatic Zone but with higher values recorded for the higher latitudes. The interpolated spatial maps of the average and total production for the two crops reveal that latitudinal increase is associated with higher levels of output with the Upper East and West regions having much of the concentration. Notably, the North-Eastern part of the Upper East region around the Bawku East, Bawku West and the Garu-Tempane districts are the areas with the highest levels of output. The concentrated high levels of production found between latitudes 10\u0026deg; and 11\u0026deg;N with which are the areas much drier with the Guinea Savannah Agro ecology (Figs. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e). Much of the regions from latitudes 8.30\u0026deg; and 10\u0026deg;N that bothers the Forest transition Zone has much less concentrated output. Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e summarises, the spatial difference millet and sorghum production.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Geostatistical measures\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\"\u003e\n \u003cp\u003eNugget\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMajor Range\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePartial Sill\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRoot-Mean-Square\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage\u003c/p\u003e\n \u003cp\u003eStandard Error\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\u003cstrong\u003eAverage Millet Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,130.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,541.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage Sorghum Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7,243.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,037.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Millet Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74,957.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86,379.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sorghum Production\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170,994.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150,355.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1992 Millet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,966.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,824.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004 Millet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4626.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,779.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015 Millet\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3112.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,493.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1992 Sorghum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9,444.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11,349.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004 Sorghum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,478.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,267.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2015 Sorghum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6,151.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,455.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 The pattern in Millet and Sorghum using the Mann-Kendall and Sen\u0026rsquo;s slope estimator\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe non-parametric Mann-Kendall test to find the presence of a monotonic increasing and decreasing trend resulted in a null S test output because the number of annual values for the test is more than 10 (55 years). On the other hand, the Z test (normal approximation) gives a positive figure (Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) which shows an upward increase for all measurement of Production, Yield and Area Harvested and ranges between 5.86 and 6.20 at a significance level of 0.001 (0.1%). The only exception to the above is Area harvested for Millet which is 0.83, in all there is a positive, upward trend.\u003c/p\u003e\n \u003cp\u003eSen\u0026rsquo;s slope estimator (Q) a linear model determines the slope of the trend and the variance of the residuals, which is constant over time. The Q estimator for production for Sorghum (4,509.38) is more than twice that for Millet (2,046.50), however, the Q measure for Area Harvested reveals a converse relationship between production and the land dedicated to it as Millet (277.78) has a much higher trend slope than Sorghum (87.64) (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMann-Kendall and Sen\u0026rsquo;s slope estimator results for Millet and Sorghum (1961\u0026ndash;2015)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePRODUCTION\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eYIELD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAREA HARVESTED\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\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\u003eMillet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMillet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSorghum\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMillet\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSorghum\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\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest S\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTest Z\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.14\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\u003e6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSignific.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,046.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,509.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e277.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQmin99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,378.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,027.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-779.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQmax99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,736.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,724.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e134.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,256.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQmin95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,561.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,499.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-465.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQmax95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,573.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,380.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e129.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,076.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78,070.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99,851.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,908.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,795.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e172,777.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,908.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBmin99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93,872.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e125,565.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,752.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,656.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e214,795.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,752.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBmax99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62,686.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57,060.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,314.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,040.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e143,446.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,314.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBmin95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89,961.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112,077.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,570.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6,454.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201,941.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,570.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBmax95\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67,214.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68,264.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,461.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,162.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149,541.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,461.62\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***Significance level =\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThe Qmin99 [(Table 5: row 5 column 6): the lower limit of the 99% confidence interval of Q (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.1) = -779.51] and the Qmin95 [(Table 5: row 7 column 6): the lower limit of the 95% confidence interval of Q (\u0026alpha;\u0026thinsp;=\u0026thinsp;0.05) = -465.31] for Area Harvested for Millet are both negative and very dissimilar as compared to the other productivity measures and for even Area Harvested for Sorghum (Table 5).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4.0 Discussion and conclusions","content":"\u003cp\u003eThe country produces just about 51% of its cereal needs [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and about half of this imported needs are imported rice and maize. However, Millet and Sorghum do not fall into this category of imported cereals as demand is wholly met by local production. The Periods of low productivity of the two crops, where the rate of change departs significantly from the general trend of increasing production level, can be attributed to climate extreme events such as droughts and its accompanying wildfires. The drought of the period 1981\u0026ndash;1983 is associated with a decline in output for the two crops of Millet and Sorghum and similar decline in output for rice and maize. Similarly, the years 1979, 1989 and 2007 also reveal such decline in output generally for agriculture [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and for Millet and Sorghum. This is as a result of decreasing monthly and annual rainfall amount which results in water deficits and agricultural drought that affects crop growth [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and bushfires that destroys large hectares of farmlands [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThough a case can be made for the favourable weather and climatic conditions as the driving factor for the years of bumper harvest recognisable by distinct peaks in the time series plots, other policy interventions are equally very important factors in achieving such milestones. It is observed that, there are periods where the increased outputs can directly be attributed directly to a particular government policy. In the Northern Savannah Agro-climatic Zone, notable policy interventions during this period which have contributed to increased productivity include the Land Conservation and Smallholder Rehabilitation Project (LACOSREP) I \u0026amp; II, Ghana Agricultural Sector Investment Programme (GASIP) -- (June 2015 - June 2019), Northern Rural Growth Productivity Programme (NRGP), West African Agricultural Productivity Programme (WAAPP). These programme interventions included farmer capacity development, provision of enhanced extension services, subsidised cost of inputs, access to cheaper equipment and machinery etc. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe increasing trend in output for Millet and Sorghum has been accompanied by a contemporaneous increase in farm sizes which calls into question an investigation into the extent to which land as an input factor and other socio-economic and environmental factors have contributed to production levels. There is therefore less appreciable inter-annual increase in Yield.\u003c/p\u003e \u003cp\u003eThere is a general upward trend generally for agricultural productivity, and these increases save some years where there was a comparative decline in output is positive and accompanied by a commensurate level of increases for cropped area / Area harvested. This makes it easy to conclude that the gains are as a result of the land take increase. However, such a conclusion would not be empirically justifiable because it does not consider the contribution of other inputs such as labour, enterprise, capital and agro-inputs like fertilizer and implements and how economic policy and implementation targeted programmes affects all other inputs and farmer motivation. We do recommend that further work that considers the efficiency of the production inputs be explored to ascertain why increases in output is accompanied by increasing cropped Area denoting agricultural expansion with little regard for intensification.\u003c/p\u003e \u003cp\u003eThe following specific attributions are made concerning the analysis of the growth of Millet and Sorghum.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eSorghum production has always been higher than Millet production for the entire duration and the rates on the increase in production have always been higher, increasing much more rapidly from the period 1987 and almost doubling the output rate for Millet.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe drier regions in the Upper East region and West regions have a much greater output than the wetter areas close to the Forest transitional Zones, and the increasing latitudinal increase corresponds to higher levels of production for both crops.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe central portions of the Northern region have very sparse production levels owing basically the large sizes of the districts and the existence of forest reserves in the areas that cover the East Gonja, West Gonja and Central Gonja districts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe Mann-Kendall test (z), apart from the yield levels where Millet has the highest upward trend than Sorghum, Sorghum has significantly higher test Z than Millet in the Production and Area Harvested. This means that Millet is cultivated much more intensively than Sorghum and much of the increases in the production of Sorghum is attributable to a commensurate increase in the Area Harvested.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003e1 - Conceived and designed the experiments; S. A. 2 - Performed the experiments; S. A., J. N. A. A., D. J.3 - Analyzed and interpreted the data; S. A., J. N. A. A., D. J. andT. A.4 - Contributed reagents, materials, analysis tools or data; S. A., J. N. A. A., D. J. T. A and L. K. 5 - Wrote the paper; S. A.6 - Review of manuscript; S. A., J. N. A. A., D. J. T. A and L. K.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eSOURCES OF DATA USED FOR THIS RESEARCH HAVE BEEN EXPLICITLY STATED IN THE MANUSCRIPT. THESE ARE PUBLIC DATASETS PROVIDED FROM REPOSITORIES ONLINE.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDourado-Neto, D., Teruel, D. A., Reichardt, K., Nielsen, D. R., Frizzone, J. A., \u0026amp; Bacchi, O. O. S. (1998). Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science. \u003cem\u003eScientia Agricola\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e, 46\u0026ndash;50. https://doi.org/http://dx.doi.org/10.1590/S0103-90161998000500008\u003c/li\u003e\n \u003cli\u003eMoeletsi, M. E., \u0026amp; Walker, S. (2012). A simple agroclimatic index to delineate suitable growing areas for rainfed maize production in the Free State Province of South Africa. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e, \u003cem\u003e162\u0026ndash;163\u003c/em\u003e, 63\u0026ndash;70. https://doi.org/http://dx.doi.org/10.1016/j.agrformet.2012.04.009\u003c/li\u003e\n \u003cli\u003eIPCC. 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T., Odoi, J. O., \u0026amp; Afrifa, E. K. a. (2010). Impact of Floods on Livelihoods and Vulnerability of Natural Resource Dependent Communities in Northern Ghana. \u003cem\u003eWater\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 120\u0026ndash;139. https://doi.org/10.3390/w2020120\u003c/li\u003e\n \u003cli\u003eAsuming-Brempong, S., \u0026amp; K. M. Kuwornu, J. (2013). Policy Initiatives and Agricultural Performance in Post-Independent Ghana. \u003cem\u003eJournal of Social and Development Sciences\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(9), 425\u0026ndash;434.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sorghum, Millet, Trend, Pattern, Mann-Kendall, Yield, Savannah, Ghana","lastPublishedDoi":"10.21203/rs.3.rs-5508540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5508540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSorghum and Millet are the two most essential kinds of cereal, among many others, grown in the Northern Savannah Climatic Zone (NSAZ) of Ghana. The zone has climatic and ecological conditions which are well- suited to the cultivation of these crops. The high dependence on the two crops as major staples has ensured that they become essential crops whose production results in food security. However, output for these crops has suffered challenges emanating from unpredictable climatic conditions and high cost of inputs. This paper investigates and compares the level of productivity and rates of change for the two crops between the period 1960 and 2015 using the Mann-Kendall (MK) trend analysis. An assessment of the spatial dynamics that identifies areas of high production between the period 1992 and 2015 carried out using ordinary kriging interpolation. Sorghum production is at a much higher level of output with a maximum production level of 387,400 metric tonnes in the year 1997 compared to the maximum of Millet which is at 245,550 metric tonnes in the year 2008. The seasonal analysis reveals a very high increase in output for both crops for the period 1990 to 2010 (season 4) and the lowest season of production from 1970\u0026ndash;1980 (season 2). Notably, the North-Eastern part of the Upper East region around the Bawku East, Bawku West and the Garu-Tempane districts are the areas with the highest levels of output which lies between latitudes 10\u0026deg; and 11\u0026deg;N which are the areas much drier. The general levels of productivity reveal that both crops have increased in output over the period of study, but Sorghum has seen a much higher rate of growth than Millet. The increase and decrease in production associated with a complimentary rise and reduction in Yield and Area harvested. The general spatial pattern of high Millet and Sorghum output and Yield varies over the entire Northern Savanah Agro-climatic Zone but with higher results recorded for the higher latitudes.\u003c/p\u003e","manuscriptTitle":"Trends and Spatial Patterns of Sorghum and Millet Production in the Northern Savannah Agro-climatic Zone of Ghana; A statistical Appraisal","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 06:30:01","doi":"10.21203/rs.3.rs-5508540/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bf81b2ed-1e19-4227-83a2-ccd13c91484c","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-19T06:30:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-19 06:30:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5508540","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5508540","identity":"rs-5508540","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}