Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia

Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia Petros Chavula, Chizumba Shepande, Samuel Feyissa, Million Sileshi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3604497/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 Background The environmental, economic, and social implications of climate change are anticipated to have a significant impact on smallholder farmers, whose way of life is heavily reliant on the environment. This study evaluates factors influencing the adoption of climate-smart agriculture practices and crop productivity among smallholder farmers in Nyimba District, Zambia. Data was collected from 194 smallholder farmers' households from June to July 2022 in twelve villages placed in four agricultural camps of Nyimba District. Four focus group discussions were also conducted to supplement data collected from the household interviews. A logistic regression model was used in this study to assess the determinants of crop production and the adoption of climate-smart agriculture in response to changes in climate and climate variations. Propensity score matching was also performed to assess the impacts of climate-smart agriculture adoption among adopters and non-adopter farming households' crop yields in the study area. Results Results from the study logit regression model indicate that the smallholder farmer’s level of education, household size, fertilizer usage, age of household head, gender, farming experience, livestock ownership, annual income, farm size, marital status of household head, and access to climate information, all affect smallholder farmers’ household’s climate-smart agriculture practices adoption and crop productivity. The study propensity scores matching analysis found that crop yield for smallholder farmers’ climate-smart agricultural practices adopters was 20.20% higher than for non-adopters. The analysis also found that implementing climate-smart agriculture practices in the study area increases maize yield for smallholder farmers adopters by 21.50% higher than non-adopters. Conclusion This study provides direction for policymakers to strengthen farmers' adaptation strategies to climate change and guide policies through the adoption of climate-smart agricultural practices. However, these practices and efforts are capable of lessening the adverse effects of changes in climate and improving agriculture production. Agronomy Adoption Agriculture Climate-smart agriculture Climate change Crop productivity Figures Figure 1 Figure 2 Figure 3 1. INTRODUCTION The climate changes are already hampering agricultural production growth for both livestock and crop production worldwide (Alfani et al., 2019 ). Increased climate variability and climate changes exacerbate production risks and challenge farmers’ coping abilities. These climate changes bring about threats to access nutritious food for urban, peri-urban, and rural communities due to reduced agricultural production and household income( Sharifi, 2021 ; Ivanova et al., 2020 ; Mossie, 2022 ), and increased risks that disrupt food markets. According to the Intergovernmental Panel on Climate Change (IPCC) 2018 report, climate change affects crop production in most parts of the world, with negative effects more common than positive, and developing countries remain extremely susceptible to further negative impacts. Increases in the frequency and intensity of extreme events such as drought, heavy rainfall, flooding, and high maximum temperatures are already occurring and are expected to accelerate in many parts of the world ( Murray & Ebi, 2012 ; IPCC, 2018 ). Average and seasonal maximum temperatures are projected to continue rising, with higher average rainfall overall. These effects will not, however, be evenly distributed. Water scarcity and drought in already dry regions are also likely to increase by the end of the 21st century. The climate changes projected to partake and/or already contribute to a worldwide reduction in cereal yields ( i.e. , maize and wheat by 3.8% and 5.5% respectively), and several researchers warn of steep decreases in crop productivity when temperatures exceed critical physiological thresholds. Smallholder farmers falling in the group of poor producers, the landless, and marginalized ethnic are all vulnerable to changes in climate (CIAT & World Bank, 2017 ; Makate, 2019 ). In addition, climate change extreme events and shocks can be long-lasting, as risk exposure and increased uncertainty affect investment incentives and reduce the likelihood of effective farm innovations while increasing that of low-risk, low-return activities. Climate change will almost certainly have a significant impact on the average yields of Zambia's major crops (maize, wheat, and sorghum), because agronomic conditions for these crops may worsen in large parts of the country ( Molieleng et al., 2021 ; Chavula, 2022 ; Stadtbäumer et al ., 2022). Climate change extreme events and shocks such as drought and flooding, do have a greater impact on crop production in Zambia and other Sub-Saharan African countries. However, through the intricacy of the agricultural diverse systems in Sub-Saharan African countries and its' inter-relation the socio-economic facets of smallholder farmers' households. An integrated approach has been promoted to maximize productivity at smallholder farmers agricultural landscape to adapt to changes in climate, these approaches and/or interventions are termed 'climate-smart agriculture (CSA)' farmers (Makate, 2019 ; Odubote & Ajayi, 2020 ; Zakaria et al., 2020 ; Molieleng et al., 2021 ). Climate-smart agriculture emerged in the late twentieth century in Zambia, when farmers began to face economic, ecological, and/or climate change challenges in line with their agriculture production. During this time, climate-smart agriculture practices primarily focused on assisting smallholder farmers in maintaining excess production levels, allowing them to become and remain active players in the agriculture industry. The emergency of CSA focused on combating the adverse impacts of the changing climate on smallholder farming households, the Republic of Zambia has embarked on the promotion of CSA practices to reclaim degraded landscapes and enhance households resilient to changes in climate and variations (Ngoma et al., 2021 ). These interventions have been conducted by the Republic of Zambia in concurrence with national and international research, and development partners partners (Ngoma et al., 2021 ). Climate-smart agriculture practices (e.g., sustainable agriculture, integrated nutrient management, organic farming, agroforestry technologies, integrated pest management, conservation agriculture, and multi-cropping systems among others) are designed to increase household income, improve agricultural production whilst promoting changing climate resilience through sustainable management of arable land and less synthetic fertilizer usage (Newell et al., 2019 ). To meet the United Nations Sustainable Development Goals number one (1) and number two (2) that is ‘No Poverty’ and ‘Zero Hunger’. Consequently, due to the importance of CSA, the Zambian government has made climate-smart agriculture practices’ promotion ( i.e. , organic farming, integrated pest management agroforestry, conservation agriculture, and integrated agriculture practices to mention a few) among the most important components of extension and rural advisory service delivery. However, this study unlike the earlier empirical studies examined the influences on the adoption of climate-smart farming practices and crop production among smallholder farmers in Nyimba district, Zambia. 1.1. Conceptual Framework Climate-smart agriculture is a strategy for changing and reorienting the agricultural landscape to promote food security in light of the emerging climatic realities variations and climate change (Chavula, 2021 ). Climate change disrupts food markets, posing population-wide risks to food production and supply. These risks can be decreased by enhancing farmers' capacity for adaptation as well as enhancing the mitigation and efficient use of agricultural production systems. Smallholder farmers who have received information on climate change and/or perceive it to be real are highly likely to adopt climate-smart agricultural practices to meet its tenets. Tenets to boost household income and productivity; increase resilience and adaptation; mitigation and reduced greenhouse gasses emissions. The adoption of climate-smart agriculture to meet its tenets is affected by institutional, cognitive, and socio-economic factors. Source: Adopted and modified from Serrat (2008) 2. MATERIALS AND METHODS 2.1. Study Area Description 2.1.1. Location The research was carried out in Nyimba district of Eastern Province Zambia. The district is situated 334 kilometers East of Lusaka Zambia's national capital. In the South the district borders with Mozambique, North with Muchinga province, West with Lusaka province, and East with Petauke district. The district lies between latitude (13 o 30‵1019‶ and 14 0 55‵81426‶ South) and longitude (30 o 48‵5047‶ and 31 0 48‵20252‵‵East). 2.1.2. Climate, Soil and topography Zambia as a country is divided into three (3) agro-ecological zones ( i.e. , Zone I, Zone II (IIa and IIb), and Zone III) of which Nyimba district falls in Zone I. Agro-ecological zone I covers the Zambezi and Luangwa River basins’ Southern and Eastern rift valleys. It also stretches to parts of Zambia’s Western and Southern provinces in the South. The district’s average annual rainfall ranges between 600 millimeters to 900 millimeters (GRZ, 1968); the wettest months are December to February, with a distinct dry season from May to November. The annual mean temperature is 24.2 o Celsius whereas the daily temperature range is 10.3 o Celsius to 36.5 o Celsius. Topographically the district is composed of hills and plateaus, soils characterized as Lithosol- Cambisols, whereas in the valleys, soils are classified as Fluvisol- Vertisols (GRZ, 1986). The elevation varies from 450-1000m at the Luangwa River valley bottom and extends to the plateau near Nyimba district center, and even higher on the mountain tops in the district’s western part. 2.1.3. Vegetation type The Miombo woodland is the most dominant formation and habitat type in Southern Africa (Gumbo and Dumas-Johansen, 2021 ; Montfort et al., 2021 ). Miombo woodland is also the major forest type in Zambia itself, covering approximately 45% of the entire land surface (Kalinda, 2008). Nyimba is located in the middle of the Miombo Ecoregion, a biome with a variety of flora types that is dominated by tree species from the Caesalpinioiae subfamily of leguminous plants (Timberlake and Chidumayo, 2011). Depending on the climate, soil, landscape position, and degree of disturbance, the ecoregion's vegetation varies in composition and structure(Timberlake and Chidumayo, 2011; Halperin et al., 2016 ). Nyimba is located in the arid ecozone and is characterised by four types of plants: Dry miombo woodland ( i.e., Brachystegia spiciformis , B. boehmii, and Julbernardia globiflora ), Mopane woodland ( i.e., Colophospermum mopan e), Munga woodland ( i.e. , Vechellia sp., Senegalia sp., Combretum sp., and trees associated with the Papilionoideae subfamily) and Riparian Forest ( i.e. , mixed tree species). 2.1.4. Land use and farming systems Nyimba district's total land area is about 10,500 square kilometers according to the population and housing census of 2010. Therefore, 82% of the district population is agrarian with average household income. These households are farmers who are into mixed agriculture practices dominating the agricultural scene in the district. However, local smallholder farmers in the district practice some sort of shifting cultivation. Under this agricultural system, crops are grown in mounds and/or ridges in most cases maize. The major crops grown include banana (Musa sp.), maize ( Zea mays ), finger millet ( Eleusine coracana ), groundnuts ( Arachis hypogaea ), haricot bean (Phaseolus vulgaris) , cowpeas ( Vigna unguiculata spp.) and soybean ( Glycine max ). Multiple cropping systems are common among farming households where cultivated land is on gently and moderately steep slopes. The topography of the land in the district makes the agricultural or cultivation pattern different from other areas. Therein, the cropping system is alongside livestock production such as cattle, goats, chickens, ducks, and doves. Besides agricultural activities, farmers are engaged in charcoal production, timber, firewood supply, and non-timber forest products (NTFPs) from the miombo woodland for household economic gain ( Policy Brief , 2016). 2.2. Site Selection An exploratory survey was conducted to collect basic information about the study area before actual data collection. Information gathered included; distance between villages, number of farming households per village, contact details for lead farmers, CSA practices adopters’ households and the location of croplands, and identifying central meeting points for focus group discussion (FGD). 2.2.1. Sampling technique This research used a multistage random sampling technique to select participants to be part of the study. This study drew smallholder farmers from agricultural camps. An agricultural camp is a delineation made by the Republic of Zambia Ministry of Agriculture containing a certain number of smallholder farmers' households in a district across villages for easy access by agriculture extension officers. From the eight (8) agricultural camps in Nyimba District, four (4) agricultural camps were randomly selected ( i.e. , Ndake, Central camp, Lwende, and Ofumaya). The total number of farmers in the selected four (4) agricultural camps in Nyimba District is 10,700. The study made use of Slovin’s formula for sample size calculation. Further, the study randomly selected three (3) villages ( i.e. , Sikwenda, Sichipale, Mawanda, Elina, Katumbila, Sichalika, Malalo, Mwenecisango, Mulivi, Lengwe, Mofu and Yona) from each camp. The study first used a margin of error of 0.05 and obtained a sample size of 386 participants. However, this sample size required more time and resources, to reduce the sample size, the study then used a margin of error of 0.1 and obtained a size of 99, as shown below. Sample size formula: Slovin’s (1960) formula. $$n=\frac{N}{1+N{e}^{2}}$$ $$n=10700/(1+10700\left({0.1}^{2}\right)$$ $$n=10700/27.75 n=99.07$$ The study therefore settled for a sample size of 194 participants, which is between the sample size of 99 (0.1 margin of error) and 386 (0.05 margin of error). Through the aid of agricultural camp officers, farmer registers for each village were used to randomly select participants in an Excel spreadsheet. 2.2.2. Focus group discussion Focused group discussions (FGD) were conducted to collect in-depth data about smallholder farmers’ factors affecting climate-smart agriculture practices (CSAP) adoption, crop productivity, perceptions on climate-smart agricultural practices, CSAP practiced as well and perceptions on climate change. This was attained via means of a developed open-ended FGD study tool. The FGDs are regarded to be better than individual interviews as sensitive issues come out during the execution. Individual farmers tend to fail to express themselves fully during a one-on-one interview. A total of four (4) FGDs were carried out in the study area comprising village headmen, women, men, and youths. The FGD meetings were held at central places for easy access by individual farmers. The core purpose of FGDs was to supplement collected data via household questionnaires. 2.3.3. Household interviews The questionnaire included closed and open-ended questions. Before carrying out the household interviews, the questionnaire was pre-tested six (6) times for appropriateness (e.g., clarity, adequacy, and question sequence), and then changed based on the results. Smallholder farmers’ households not participating in the survey were used in pre-testing the questionnaires. The study engaged three Enumerators who were taught and overseen by the principal researcher, with experience in data collection. Collected data was verified and amended after each fieldwork day and backed on CSPRO Cloud. 2.4. Variables specification Outcome Variables The outcome variable for this study is the impact of CSAP adoption among smallholder farmers' households' crop productivity. And factors influencing crop productivity and CSAP adoption among smallholder farmers in Nyimba district. Dependent Variables Smallholder farmers’ household decision to adopt CSAPs The dependent variable was the smallholder farmers’ household to adopt CSAPs taking a value of one (1) and zero (0) if the smallholder farmers’ household does not adopt. The main reason was to identify elements that influence the adoption of CSAPs among smallholder farmers’ households in the Nyimba district, Zambia. Independent Variables Variable description, measurements, and expected sign Independent variables AGEHH Age of household head Continuous (Years) + SEXHH Gender of household head Dummy (1 if male, 0 if female) + EDUHH The educational level of household Continuous (Number of years) + HHSZ Household size Continuous (Adult equivalent) + FEXCS Extension services Dummy (1 if yes, 0 if no) - ACRE Access to credit Dummy (1 if yes, 0 if no) + TLU Total livestock unit Continuous (TLU) + INC Household annual income Continuous (Number) + MST Marital status Dummy (1 if yes, 0 if no) + FERT Fertilizer use Continuous (Number) + FMS Farmland size Continuous (Hectares) - INFO Climate information Continuous (1 if yes, 0 if no) - EXPFRM Farming experience Continuous (Years) + 2.5. Propensity score matching Propensity score matching (PSM) method was used in this study to determine the effect of CSAPs on crop productivity among adopters and non-adopters. Propensity score matching is a way of correcting treatment effect estimates by adjusting for confounding variables across a sampled population. According to Caliendo and Kopeinig (2008), there are steps in implementing PSM for a study. These are an estimation of the propensity scores using a binary model, choosing a matching algorithm, checking on common support conditions, and testing the matching quality of the treatment and/or participants. Step 1 Model Specification The Logit model in this research, is preferred due to the consistency of parameter estimation associated with the assumption that the error term in the equation has a logistic distribution (Baker, 2000; Ravallion, 2001). Therefore, the Logit model was used to estimate the probability of smallholder farmers’ adoption of CSAPs allotted to socio-economic, agroecological, and institutional characteristics. Therein, a dependent variable considered a value of 1 for CSAP adoption and 0 for non-CSAP adopters. $${P}_{i}=P\left(Y=1|X\right)$$ 1 In line with Pindyck and Rubinfeld (1981), the cumulative logistic probability function is specified as follows; $${P}_{i}=F\left({Z}_{i}\right)=F\left[a\right.+\sum _{i=1}^{m}{\beta }_{i}{X}_{i}]=\left[\frac{1}{1+{e}^{-(a+\sum {\beta }_{i}{X}_{i}}}\right]$$ 2 where e represents the base of natural logs, X i represents the i th explanatory variable, P i is the probability that a household adopted CSAP, and α and β i are parameters to be estimated. Interpretation of coefficients is made easier if the logistic model can be written in terms of the odds and log of odds (Gujarati, 1995). The odds ratio implies the ratio of the probability that an individual will be a participant ( P i ) to the probability that he/she will not be a participant (1-P i ). The probability that he/she will not be a participant is defined by: $$\left(1-{P}_{i}\right)=\frac{1}{1+ {e}^{zi}}$$ 3 $$\left(\frac{{P}_{i}}{1+ {P}_{i}}\right)=\left[\frac{1+{e}^{zi}}{1+ {e}^{-zi}}\right]={e}^{zi}$$ 4 Alternatively, $$\left(\frac{{P}_{i}}{1+ {P}_{i}}\right)=\left[\frac{1+{e}^{zi}}{1+{e}^{-zi}}\right]={e}^{\left[a+ \sum {B}_{i}{X}_{i}\right]}$$ 5 Taking the natural logarithms of Eq. (3.5) will give the logit model as indicated below. $${Z}_{i}=ln\left(\frac{{P}_{i}}{1-{P}_{i}}\right)=a+{B}_{1}{X}_{1i}+{B}_{2}{X}_{2i}+\dots {B}_{m}{X}_{mi}$$ 6 I consider a disturbance term, µ i , and the logit model becomes $${Z}_{i}=a+{\sum }_{t=1}^{m}{B}_{t}{X}_{ti}+{\mu }_{i}$$ So the binary logit will become: $$Pr\left(pp\right)=f\left(X\right)$$ 7 Where pp is CSAPs adoption, f(X) is the dependent variable project participation and X is a vector of observable covariates of the households. The dependent variable will take a value of 1 for CSAP adoption and 0 for non-adopters. In addition to the estimated coefficients, the marginal effects of the change in the explanatory variables on the probability of CSAP adoption are also estimated. The interpretation of these marginal values will be dependent on the unit of measurement for explanatory variables. However, when the explanatory variable is a dummy, the marginal effects generally produce a reasonable approximation to the change in the probability that Y = 1, at a point such as the regressors' average. Step 2 Defining the Region of Common Support and Balancing Tests The region of common support needs to be defined where distributions of the propensity score for treatment and comparison group overlap. Sampling bias may still occur, however, if the dropped CSAP non-adopters’ observations are systematically different in terms of observed characteristics from the retained non-adopters; these differences should be monitored carefully to help interpret the treatment effect. Balancing tests can also be conducted to check whether, within each quantile of the distribution of propensity scores, the average propensity score and mean of X are the same. For PSM to work, the comparison and treatment groups must be balanced in that similar propensity scores are based on similar observed X . The distributions of the treated group and the comparator must be similar, which is what balance mplies. Formally, one needs to check if \(\text{ˆ}\text{P}\left(X|T=1\right)= \text{ˆ}\text{P}\left(X|T=0\right)\) . Step 3 Matching Adopters to Non-adopters The third step is to choose an algorithm for data matching available. Matching is a common method for deciding on control subjects who are matched to the treated subjects based on context covariates that the investigator believes need to be monitored. Different ones may employ matching standards. to assign adopters to non-adopters based on propensity score. The most common matching algorithms are nearest neighbor matching (NN), radius matching (RM), and kernel-based matching (KBM). Step 4 Matching Quality In the fourth step matching quality tests could be done. Checking for matching regardless of quality the matching method can balance the distribution of various variables or not. If differences exist, there may be an indication of incomplete matching, and remedial actions are suggested (Caliendo& Kopeinig, 2008). The following step is to check whether the treatment introduced a distinction in the indicators of impact. The average treatment effect at the treated (ATT) is given by the distinction within the mean outcome of matched adopters and nonadopters that have common support conditional at the propensity score. Step 5 Sensitivity Analysis Finally, a sensitivity analysis will be carried out to check the conditional independence assumption strength. Sensitivity analysis also will be utilized to look at whether an unmeasured variable's effect on the choice process is strong enough to jeopardize the matching approach (Ali & Abdulai, 2010). The Rosenbaum bound sensitivity test will be used to carry out the sensitivity analysis (r-bounds test). 2.6. Ethical Clearance The study was solely conducted by the researcher and two supervisors with integrity, objectivity, openness, respect for research participants, respect for intellectual property, confidentiality, informed consent, fidelity, and honesty. The researcher and two supervisors take responsibility for any actions, and publications, and only make agreements intended for keeps. However, the study was approved by the University of Zambia Directorate of Research and Graduate Studies (NASREC) and bears a NASREC IRB No. 00005465 ( IORG No. 0005376) . The principal researcher did not intentionally engage in or participate in any form of malicious harm to research participants’ personal information as well as breaching the University of Zambia research principles and research ethics for Haramaya University. 3. RESULTS AND DISCUSSION 3.1. Effect of Climate-Smart Practices on Crop Productivity among Smallholder Farmers, in Nyimba, Zambia The household survey comprised 194 smallholder farmer participants, from the research area, who were chosen at random. The smallholder farmers were interviewed about crop production and their applications of various CSA practices. The study presents the household survey's findings, starting with the demographic characteristics of the participants (Table 1 ), crop production and productivity, adoption of CSA, constraints on the adoption of CSA practices, effects of CSA practices on crop productivity, and factors affecting crop productivity. The study obtained a total of 339 field plots of various crops from the 194 farmer participants. From the results in Table 1 , the study obtained that the mean age for the respondents was 46 years of age, with a standard deviation of 14.59. The majority (62.18%) were male-headed households, and 69.43% were married. The mean years of formal education was found to be 5.49 years, with a standard deviation of 3.5. The mean years of farming was found to be 26.22, with a standard deviation of 15.55. Concerning the years of living in the areas, the mean was 30.92, and the standard deviation was 18.68. The average family size was 5.42, with a standard deviation of 2.14. The average total annual income was revealed to be K5472.68 (USD 331.68) (K16.5 per 1 USD), and the standard deviation of 7626.52. And 57.51% reported participating in any off-farm activities. While 78.76% of the smallholder farmer participants reported using improved seed varieties for farming, and the average farm size (landholding) was 3.396 ha, with a standard deviation of 3.363. The land tenure system was all customary land (100%). The mean cultivated land was 1.83 ha and 1.45 standard deviation. The average number of crops grown by smallholder farmers was 2, with a standard deviation of 0.930. Table 1 Characteristics of the participant smallholder farmers Variable Mean Std. Deviation HH Head Age 46.181 14.593 HH Head Sex Male: 62.18% (120) Marital Status Married: 69.43% (134) Years of formal education 5.487 3.499 Years of farming 26.218 15.545 Years of living in the area 30.917 18.680 Household size 5.420 2.137 Total Annual Income 5472.689 7626.52 Participation in any off-farm activity Yes: 57.51% (111) Used Improved Maize Seed Yes: 78.76% (152) Farm Size (ha) 3.396 3.363 Land tenure system (Customary) 100% (194) Cultivated land (2021/2022), ha 1.828 1.448 Number of Crops (2021/2022) 2 0.930 With regards to the crops grown by the farmers, the study found that maize was the most grown crop, reported in 194 crop plots, followed by Groundnuts, reported in 99 plots, then sunflower in 69 plots, and soya beans in 16 plots (Table 2 ). The other crops; Cowpea, Bambara nuts, Cotton, Millet, and Sweet Potatoes were reported to have been grown in a few plots. Table 2 Crops grown by smallholder farmers Crops Grown Frequency Percent Cumulative Maize 194 50.13 50.13 Soybeans 16 4.13 54.26 Groundnuts 99 25.58 79.84 Cowpea 2 0.52 80.36 Bambara nuts 2 0.52 80.88 Sunflower 69 17.83 98.71 Cotton 1 0.26 98.97 Sweet potatoes 3 0.78 99.74 Millet 1 0.26 100 Total 387 100 From the results obtained, pot-holing (basin) was implemented in 61 field plots (17.99%), multi-cropping in 50 plots (14.75%), minimum tillage in 34 plots (10.03%), ripping in 32 plots (9.44%), crop rotation in 18 plots (5.31%), and manure in 11 plots (3.24%) as well as alley cropping in 9 plots (2.65%) (Table 3 ). The other CSA practices were implemented in a few plots less than ten. Table 3 Climate-smart agriculture practices adopted by smallholder farmers CSA Practices Frequency Percent Ripping 32 9.44 Basin 61 17.99 Crop rotation 18 5.31 Crop residue 2 0.59 Alley cropping 9 2.65 Multi cropping 50 14.75 Contour ploughing 6 1.77 Compost 5 1.47 Manure field 11 3.24 Zero tillage 34 10.03 Bunding 2 0.59 Concerning the number of CSA adopted, no single CSA practice was implemented in 167 plots (49.26%), one CSA practice was implemented in 123 plots (36.28%), two CSA practices were implemented in 43 plots (12.68%), 4 plots had three different CSA practices implemented, and only 1 plot had four CSA practices implemented and another plot with five CSA practices implemented (Table 4 ). Based on these results, farmers’ implementation of many CSA practices in a single plot was found to be very low. Table 4 Number of climate-smart agriculture practices adopted by smallholder farmers No._CSA_Adopted/Plot Freq. Percent Cum. 0 167 49.26 49.26 1 123 36.28 85.55 2 43 12.68 98.23 3 4 1.18 99.41 4 1 0.29 99.71 5 1 0.29 100 Total 339 100 From the study results below (Table 5 ), Maize, Groundnuts, Sunflower, and Soya beans were the most grown crops by the farmers. The mean quantity of harvest for all crops was 1223.51 Kg with a standard deviation of 1442.82. The mean quantity of maize harvested for maize was 1766.57 Kg with a standard deviation of 1594.23, while the mean quantity of Groundnuts harvested was 511.08 Kg with a standard deviation of 605.07, a mean quantity of 609.67 Kg with a standard deviation of 513.02 for sunflower, while for soya beans the mean quantity harvested was 1007.5 Kg with standard deviation of 1835.615 (Table 5 ). Table 5 Quantities harvested for various crops (kg) Variable Obs Mean Std. Dev. Min Max All Crops 339 1223.51 1442.82 50 9450 Maize 173 1766.57 1594.23 165 9450 Groundnuts 85 511.08 605.07 50 3450 Sunflower 61 609.6721 513.0212 50 2800 Soya beans 14 1007.5 1835.615 200 7245 Concerning the productivity of various crops, the overall yield per hectare of all the crops was 1316.60 kg with a standard deviation of 1214.13 (Table 6 ), while the yield per hectare for maize was found to be at 1682.52 kg per hectare with a standard deviation of 1325.87, and for groundnuts, the mean yield per hectare was found to be 822.90 kg with a standard deviation of 547.88, for sunflower, the mean yield was 962.79 kg with a standard deviation of 437.38, and for soya beans, the mean yield per hectare was 808.40 kg, with a standard deviation of 426.74. Table 6 Productivity of various crops (Yield (Kg) per hectare) Yield per hectare (Kg) Obs Mean Std. Dev. Min Max All Crops 339 1316.60 1214.13 106.67 11630.67 Maize 173 1682.54 1325.87 119.00 11630.67 Groundnuts 85 822.9003 547.8818 106.6667 2500 Sunflower 61 962.7869 437.3807 300 2000 Soya beans 14 808.4048 426.7391 200 1740 The study investigated how climate-smart agriculture techniques affected smallholder farmers' crop yield. The study found that crop yield for CSA adopters was 20.20% higher than for CSA non-adopters (Table 7 ). The results were statistically significant at 0.027 p-values (p < 0.05). This entails that adopting CSA practices increases crop yield. Table 7 Impact of climate-smart Practices on Crop Productivity among Smallholder Farmers Treatment-effects estimation Number of Obs = 194 Estimator: propensity-score matching Matches: requested = 1 Outcome model: matching min = 1 Treatment model: logit max = 2 log_yield Coef. AI Robust Std. Err. Z P > z [95% Conf. Interval] ATE CSA_Practice (Adopters vs Non_Adopters) .2019652 .0911943 2.21 0.027** .0232276 .3807028 Significance codes: ***<1%, **<5% and *<10%; Author’s calculation using Stata 15MP The study conducted a propensity score matching analysis to specifically find out how the CSA affects maize productivity (Table 8 ). The research showed that implementing CSA increases maize yield for adopters by 21.50% higher than the non-adopters. This shows that adopting CSA practices significantly increases maize yield. The results were statistically significant at 0.035 p-values (p < 0.05). Table 8 Impact of climate-smart Practices on maize productivity among smallholder farmers Treatment-effects estimation Number of Obs = 194 Estimator: propensity-score matching Matches: requested = 1 Outcome model: matching min = 1 Treatment model: logit max = 1 log_yield Coef. AI Robust Std. Err. Z P > z [95% Conf. Interval] ATE CSA_Practice (Adopters vs Non_Adopters) 0.215012 0.101795 2.11 0.035** 0.015496 0.414527 Significance codes: ***<1%, **<5% and *<10%; Authors’calculation using Stata 15MP The study conducted a logistic regression analysis to determine factors affecting the adoption of CSA practices. According to the study, age has a favorable impact on the adoption of CSA practices, the higher the age, the more likely a farmer will adopt CSA practices, statistically significant at 0.0000 p-value (p 55 years to have adopted more CSAPs in the study area. A study by Saha et al. ( 2019 ), from the logit model indicated that a farmer's level of education, occupation, family size, cultivated farm size, farmers choice of adaptation techniques for climate change is influenced by their level of agricultural experience, ownership of cattle, annual income, market difficulty, access to farm information, training background, affiliation with organizations, and perception of climate change. The study discovered that adopting CSA practices is influenced by previous farming experience, the more years a farmer spends in farming, the less likely a farmer will use CSA practices, statistically significant at 0.0000 p-value (p < 0.001). Income was found to have a statistically positive effect on the adoption of CSA practices, the greater a farmer's income level, a farmer is more likely to adopt CSA practices, statistically significant at 0.0640 p-value (p < 0.1) (Table 9 ). Zakaria et al. ( 2020 ) also demonstrated that the intensity of farmers’ adoption of climate-smart agricultural technologies is positively influenced by farmers’ experience in rice cultivation, access to mass media, training, and perceived decrease in the quantity of rain. On the other hand, the size of the farm, the distance between the farmers' homes and the farm sites, the location, and the reported rise in temperature all hurt the farmers' intensity of technology adoption. Gender in this study was found statistically significant at 0.0660 p-values (p < 0.1). Farm size was also found to have a negative significant effect on climate-smart agricultural practices adoption at 0.0050 (p < 0.01). Livestock quantity was also found to have a significant effect on climate-smart agriculture adoption at 0.0180 p-value (p < 0.1) whilst access to climate information had a negative influence on climate-smart agriculture adoption p-value 0.0060 (p < 0.01). A study by Kurgat et al. ( 2020 ) showed that female ownership of farm assets, farm location, and household resources were major determinants of climate-smart agricultural adoption in Tanzania. Aryal et al. ( 2018 ) concluded that several factors, such as household characteristics, market access, and main climate hazards are found to affect the probability and level of implementing different climate-smart practices of climate-smart agricultural adoption by smallholder farmers. A similar study by Abegunde et al. ( 2019 ) On the other hand; marital status, education, fertilizer, credit access, and access to extension services were found not to have a significant effect on the adoption of CSA practices. Table 9 Factors affecting smallholder farmers’ adoption of climate-smart agricultural practices Logistic regression Number of Obs = 194 Wald chi2(10) = 27.34 Prob > chi2 = 0.0112 Log pSeudolikelihood = -204.0124 Pseudo R2 = 0.0965 CSA_Practice Coef. Robust Std. Err. z P > z [95% Conf. Interval] Age 0.085697*** 0.0222 3.8600 0.0000 0.0422 0.1292 Gender 0.017260* 0.4056 0.4400 0.0660 0.7776 0.8122 Marital_status -0.178756 0.1399 -1.2800 0.2010 -0.4530 0.0955 Education -0.051048 0.0387 -1.3200 0.1870 -0.1270 0.0249 Farming_experience 0.087116*** 0.0200 -4.3600 0.0000 -0.1263 -0.0480 Household_size -0.027906 0.0658 -0.4200 0.6720 -0.1569 0.1011 Income 0.000035* 0.0000 1.8500 0.0640 0.0000 0.0001 Fertilizer 0.000727 0.0007 1.1200 0.2630 -0.0005 0.0020 Farm_size -0.02006** 0.0449 -0.4500 0.0050 -0.1082 0.0680 Livestockqt 0.006734* 0.0083 0.8100 0.0180 -0.0230 0.0095 Credit_access -0.150782 0.2405 -0.6300 0.5310 -0.6221 0.3205 Access_to_climate_inform -0.44108** 0.5920 -0.7500 0.0060 -1.6014 0.7192 Extension_services -0.018090 0.2964 -0.0600 0.9510 -0.5989 0.5628 _cons -0.416121 1.0016 -0.4200 0.6780 -2.3792 1.5470 Significance codes: ***<1%, **<5% and *<10%; Author’s calculation using Stata 15MP The study carried out Cobb Douglas production analysis to determine factors affecting the productivity of crops (Table 10 ). Study results showed that income has a positive significant impact on crop productivity, productivity improves by 0.002% the outcome of farmers' increase in income level, statistically significant at 0.0040 p-value (p < 0.01). A study by Urgessa, ( 2015 ) showed that the most important factors that influence agricultural labor are determined to be the land-labor ratio, fertilizer and pesticide use, manure use, and household size. and land productivity ( i.e. crop productivity). According to WenJing et al. ( 2021 ) study of income differences across households’ quartiles, the study found households with high on-farm income are more sensitive about enlarging their farm size by renting farmland, and households with middle and upper-middle off-income may benefit more from renting out their farmland. Fertilizer was found to have a significant positive impact on crop productivity. A unit increase in fertilizer use was associated with a 0.12% increase in crop yield, statistically significant at 0.0000 p-values (p < 0.001). Du et al. ( 2020 ) indicated long synthetic fertilizer and manure application have benefits on crops produced and soil production. Farm size was found to harm crop productivity, the bigger the farm size, the lower the crop productivity by 6.52%. Livestock quantity was found to have a positive significant influence on crop productivity, the higher the number of livestock a farmer has, the higher the yield of crops by 0.86%, statistically significant at 0.0001 p-value (p < 0.001). Livestock provides farming households with manure and animal draught power to produce crops and the investment of income from livestock into technologies that benefit crop production (Anderson, 1989 ). In addition, to the effects of manure and draught on crop output; money from livestock is frequently invested in terms that improve crop production. Adopting CSA practices was found to have a profoundly favorable effect on crop productivity, if one more farmer adopts CSA practices, the average yield for the farmers improves by 13.49%, statistically significant at 0.0720 p-values (p < 0.1). The other factors were found not to have a significant impact on crop yield. Mujeyi et al. ( 2021 ) found similar results on the adoption of climate-smart agriculture to significantly contribute to the crop yield of smallholder farmers on an integrated crop-livestock system. Marital status influences crop productivity by 0.07% whilst the education level of the household head had a 0.098% influence on contribution crop productivity among smallholder farmers. Household size also contributed 0.2% to smallholder farmers crop productivity. A similar study by Serote et al. ( 2021 ) according to the findings, smallholder farmers' household demographics characteristics and institution characteristics influenced the adoption of climate-smart agriculture and crop productivity. Table 10 Factors affecting smallholder farmers’ crop productivity Linear regression Number of Obs = 194 F(9, 179) = 11.05 Prob > F = 0.0000 R-squared = 0.6441 Root MSE = 0.74495 log_yield Coef. Robust Std. Err. t P > t [95% Conf. Interval] Age -0.00192 0.0051 -0.3700 0.7090 -0.0120 0.0082 Gender 0.03854 0.1126 0.3400 0.7320 -0.1830 0.2601 Marital_status 0.00755* 0.0379 0.8410 0.0220 -0.0821 0.0670 Education 0.00980* 0.0122 0.8000 0.0420 -0.0338 0.0142 Farming_experie ~ e 0.00434 0.0049 0.8800 0.3770 -0.0053 0.0140 Household_size 0.02308** 0.0181 1.2800 0.0012 -0.0586 0.0124 Income 0.00002** 0.0000 2.9400 0.0040 0.0000 0.0000 Fertilizer 0.00123*** 0.0002 8.1300 0.0000 0.0009 0.0015 Farm_size -0.06518** 0.0145 -4.4900 0.0000 -0.0938 -0.0366 Livestockqt 0.00863** 0.0018 4.7900 0.0000 0.0051 0.0122 CSA_Practice 0.13490* 0.0747 1.8100 0.0720 -0.0120 0.2818 Credit_access -0.11707 0.0741 -1.5800 0.1150 -0.2629 0.0287 Access_to_climate Inform -0.15234 0.1974 -0.7700 0.4410 -0.5408 0.2361 Extension_services 0.04293 0.0846 0.5100 0.6120 -0.1236 0.2094 __cons 7.19355 0.3095 23.2400 0.0000 6.5845 7.8026 Significance codes: ***<1%, **<5% and *<10%; Author’s calculation using Stata 15MP 3.2.1. Climate-smart practices impact on crop productivity The objective of the study was to determine the impact of CSA practices adoption on crop productivity among smallholder farmers in Nyimba district, Zambia. The study also determined factors affecting crop and adoption of climate-smart practices (CSAPs) among smallholder farmers in Nyimba district, Zambia. Based on data analysis, the study found that smallholder farmers’ crop yields of CSAP adopters were 20.20% higher than for non-adopters. The study also found that implementing CSAPs increases maize yield for smallholder farmers adopters by 21.50% higher than non-adopters. This clearly shows that adopting climate-smart agricultural practices significantly increases crop productivity for smallholder farmers. Similarly, Abegunde et al. ( 2022 ) carried out a study on the effect of climate-smart agriculture on household food security, with 327 smallholder farmers sampled through a multi-stage technique, and employed binary logistic and multinomial logistic regression models. The findings revealed that CSA practices adaption significantly and favorably affects household food security. The findings also indicated that agricultural revenue and income from non-farm sources had a significant and favorable impact on household food security (Abegunde et al., 2022 ). Mossie ( 2022 ) also conducted a similar study on the impact of climate-smart agriculture technology on productivity in Southern Ethiopia. The study analyzed data using propensity score matching to determine the technology impact on adopters and non-adopters. According to the findings, wheat row planting has a favorable and meaningful effect on productivity. The study also found that smallholder farmers who sowed wheat in planting rows produced 1368 kg of wheat per hectare compared to the counterfactual scenario. Therefore, the CSA practice of row planting had a significant impact on yield (Mossie, 2022 ). Tadesse et al. ( 2021 ) conducted a study on the impact of climate-smart agriculture in Ethiopia on soil carbon, crop productivity, and fertility. The results showed that yield was 30–45% higher under CSA practices than the control (p < 0.05). The pH of the soil was very minimally raised by CSA interventions, and the amount of total nitrogen and phosphorus that was available to plants increased by 2.2–2.6 and 1.7–2.7 times, respectively, in comparison to the control. The research shows that by increasing crop yield and minimizing nutrient depletion, CSA practices can help resource-poor farmers become more resilient to climate change. (Tadesse et al., 2021 ). A study by Kichamu-Wachira et al., ( 2021 ) on the effects of climate-smart agricultural practices on crop yields, soil carbon, and nitrogen pools in Africa. The study revealed similar results that the implementation of CSAP significantly increased crop yields among smallholder farmers in Africa. The study also further concluded that CSAPs are an alternative advanced agricultural technology as compared to conventional farming typologies due to their enhancement of food production through climate mitigation and soil quality. Furthermore, Amadu et al. ( 2020 ) sampled 808 smallholder farmers' households in southern Malawi in the project area and found that 53% of CSAP adopters had increasing yields of maize in the drought year of 2016. Fentie and Beyene's (2019) research findings from the PSM model revealed that the adoption of CSAPs had a significant impact on crop yield per hectare. Therefore, scaling up CSA will significantly contribute to farmers' resilience to adverse effects of the changing climate and climate variations by enhancing crop productivity and food security among farming households. Beedy et al .(2010) espoused the significant and positive influence of Gliricidia sepium Alley cropping on soil organic matter influence on a compiled single field of maize. Alley cropping had impacts on the changes in soil physicochemical properties enhanced maize yields, and increased soil nutrients over the mid and long term. CONCLUSION Climate-smart agriculture holds a promise for humankind and the earth's planet, it can be successful if all developed and developing countries produce more food while generating less environmental pressure. According to the results of this study, adopting climate-smart farming practices can significantly improve the welfare of smallholder farmers. Consequently, the research recommends that many more interventions are warranted to ensure that impoverished and vulnerable smallholder farmers' households have access to improved agricultural technologies. Through better coordination of their separate actions, key stakeholders can use the study's findings to assist smallholder farmers’ households in the study area and Zambia who are experiencing adoption issues. Declarations Ethics approval and consent to participate Applicable through the University of Zambia Postgraduate Research. Consent for publication Not applicable Availability of data and materials The data for this research is available upon request. Furthermore, all the data that was reviewed for this paper have been published elsewhere. References including doi codes, as available, are availed accordingly. Competing interests The authors declare no conflict of interest Funding Funding was provided by the World Bank through the African Centre of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University, Ethiopia Author contributions Petros Chavula: -MSc student conducted the study and wrote the manuscript Dr. Chizumba Shepande and Dr. Samuel Feyissa edited the manuscript whilst Dr. Million Sileshi did the proofreading of the manuscript. References Abegunde, V. O., Sibanda, M., & Obi, A. (2019). The dynamics of climate change adaptation in sub-Saharan Africa: A review of climate-smart agriculture among small-scale farmers. Climate , 7 (11). https://doi.org/10.3390/cli7110132 Abegunde, V. O., Sibanda, M., & Obi, A. (2022). Effect of climate-smart agriculture on household food security in small-scale production systems: A micro-level analysis from South Africa. Cogent Social Sciences , 8 (1), 2086343. 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Earth Systems and Environment , 4 (1), 257–271. https://doi.org/10.1007/s41748-020-00146-w 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About In Review Editorial Policies 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-3604497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":248803834,"identity":"68f5c4c4-2a58-4ef5-9eb6-789a4104087a","order_by":0,"name":"Petros 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(2008)\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3604497/v1/1901b3cd216d78db622c5741.jpeg"},{"id":46433879,"identity":"c8e3ea35-7ae7-41c8-bef9-0e6a143f2e5a","added_by":"auto","created_at":"2023-11-14 17:10:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":374322,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area\u003c/p\u003e\n\u003cp\u003eSource: Author’s sketch with Arc GIS\u003c/p\u003e","description":"","filename":"Figure2.MapoftheStudyArea.png","url":"https://assets-eu.researchsquare.com/files/rs-3604497/v1/14644e08bc44430ed4183627.png"},{"id":46433878,"identity":"9c116f40-bf93-4d90-aa34-f95bde56d763","added_by":"auto","created_at":"2023-11-14 17:10:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":51267,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual rainfall and temperature for the study area\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3604497/v1/f536f1deb0b038cf9a5a36fd.png"},{"id":46435187,"identity":"55720fc5-2a5d-4bd9-86d7-64bd66e67162","added_by":"auto","created_at":"2023-11-14 17:26:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1120309,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3604497/v1/f0080a91-d22d-4c3f-95cb-f7ac13fbdb7a.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003e\u003cstrong\u003eFactors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe climate changes are already hampering agricultural production growth for both livestock and crop production worldwide (Alfani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Increased climate variability and climate changes exacerbate production risks and challenge farmers\u0026rsquo; coping abilities. These climate changes bring about threats to access nutritious food for urban, peri-urban, and rural communities due to reduced agricultural production and household income( Sharifi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ivanova et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mossie, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and increased risks that disrupt food markets. According to the Intergovernmental Panel on Climate Change (IPCC) 2018 report, climate change affects crop production in most parts of the world, with negative effects more common than positive, and developing countries remain extremely susceptible to further negative impacts. Increases in the frequency and intensity of extreme events such as drought, heavy rainfall, flooding, and high maximum temperatures are already occurring and are expected to accelerate in many parts of the world ( Murray \u0026amp; Ebi, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; IPCC, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Average and seasonal maximum temperatures are projected to continue rising, with higher average rainfall overall. These effects will not, however, be evenly distributed. Water scarcity and drought in already dry regions are also likely to increase by the end of the 21st century.\u003c/p\u003e \u003cp\u003eThe climate changes projected to partake and/or already contribute to a worldwide reduction in cereal yields (\u003cem\u003ei.e.\u003c/em\u003e, maize and wheat by 3.8% and 5.5% respectively), and several researchers warn of steep decreases in crop productivity when temperatures exceed critical physiological thresholds. Smallholder farmers falling in the group of poor producers, the landless, and marginalized ethnic are all vulnerable to changes in climate (CIAT \u0026amp; World Bank, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Makate, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, climate change extreme events and shocks can be long-lasting, as risk exposure and increased uncertainty affect investment incentives and reduce the likelihood of effective farm innovations while increasing that of low-risk, low-return activities. Climate change will almost certainly have a significant impact on the average yields of Zambia's major crops (maize, wheat, and sorghum), because agronomic conditions for these crops may worsen in large parts of the country ( Molieleng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chavula, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Stadtb\u0026auml;umer \u003cem\u003eet al\u003c/em\u003e., 2022). Climate change extreme events and shocks such as drought and flooding, do have a greater impact on crop production in Zambia and other Sub-Saharan African countries.\u003c/p\u003e \u003cp\u003eHowever, through the intricacy of the agricultural diverse systems in Sub-Saharan African countries and its' inter-relation the socio-economic facets of smallholder farmers' households. An integrated approach has been promoted to maximize productivity at smallholder farmers agricultural landscape to adapt to changes in climate, these approaches and/or interventions are termed 'climate-smart agriculture (CSA)' farmers (Makate, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Odubote \u0026amp; Ajayi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zakaria et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Molieleng et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate-smart agriculture emerged in the late twentieth century in Zambia, when farmers began to face economic, ecological, and/or climate change challenges in line with their agriculture production. During this time, climate-smart agriculture practices primarily focused on assisting smallholder farmers in maintaining excess production levels, allowing them to become and remain active players in the agriculture industry. The emergency of CSA focused on combating the adverse impacts of the changing climate on smallholder farming households, the Republic of Zambia has embarked on the promotion of CSA practices to reclaim degraded landscapes and enhance households resilient to changes in climate and variations (Ngoma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These interventions have been conducted by the Republic of Zambia in concurrence with national and international research, and development partners partners (Ngoma et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate-smart agriculture practices (e.g., sustainable agriculture, integrated nutrient management, organic farming, agroforestry technologies, integrated pest management, conservation agriculture, and multi-cropping systems among others) are designed to increase household income, improve agricultural production whilst promoting changing climate resilience through sustainable management of arable land and less synthetic fertilizer usage (Newell et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To meet the United Nations Sustainable Development Goals number one (1) and number two (2) that is \u0026lsquo;No Poverty\u0026rsquo; and \u0026lsquo;Zero Hunger\u0026rsquo;.\u003c/p\u003e \u003cp\u003eConsequently, due to the importance of CSA, the Zambian government has made climate-smart agriculture practices\u0026rsquo; promotion (\u003cem\u003ei.e.\u003c/em\u003e, organic farming, integrated pest management agroforestry, conservation agriculture, and integrated agriculture practices to mention a few) among the most important components of extension and rural advisory service delivery. However, this study unlike the earlier empirical studies examined the influences on the adoption of climate-smart farming practices and crop production among smallholder farmers in Nyimba district, Zambia.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Conceptual Framework\u003c/h2\u003e \u003cp\u003eClimate-smart agriculture is a strategy for changing and reorienting the agricultural landscape to promote food security in light of the emerging climatic realities variations and climate change (Chavula, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Climate change disrupts food markets, posing population-wide risks to food production and supply. These risks can be decreased by enhancing farmers' capacity for adaptation as well as enhancing the mitigation and efficient use of agricultural production systems. Smallholder farmers who have received information on climate change and/or perceive it to be real are highly likely to adopt climate-smart agricultural practices to meet its tenets. Tenets to boost household income and productivity; increase resilience and adaptation; mitigation and reduced greenhouse gasses emissions. The adoption of climate-smart agriculture to meet its tenets is affected by institutional, cognitive, and socio-economic factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSource: Adopted and modified from Serrat (2008)\u003c/p\u003e \u003c/div\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area Description\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1. Location\u003c/h2\u003e \u003cp\u003eThe research was carried out in Nyimba district of Eastern Province Zambia. The district is situated 334 kilometers East of Lusaka Zambia's national capital. In the South the district borders with Mozambique, North with Muchinga province, West with Lusaka province, and East with Petauke district. The district lies between latitude (13\u003csup\u003eo\u003c/sup\u003e30‵1019‶ and 14\u003csup\u003e0\u003c/sup\u003e55‵81426‶ South) and longitude (30\u003csup\u003eo\u003c/sup\u003e 48‵5047‶ and 31\u003csup\u003e0\u003c/sup\u003e48‵20252‵‵East).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2. Climate, Soil and topography\u003c/h2\u003e \u003cp\u003eZambia as a country is divided into three (3) agro-ecological zones (\u003cem\u003ei.e.\u003c/em\u003e, Zone I, Zone II (IIa and IIb), and Zone III) of which Nyimba district falls in Zone I. Agro-ecological zone I covers the Zambezi and Luangwa River basins\u0026rsquo; Southern and Eastern rift valleys. It also stretches to parts of Zambia\u0026rsquo;s Western and Southern provinces in the South. The district\u0026rsquo;s average annual rainfall ranges between 600 millimeters to 900 millimeters (GRZ, 1968); the wettest months are December to February, with a distinct dry season from May to November. The annual mean temperature is 24.2\u003csup\u003eo\u003c/sup\u003e Celsius whereas the daily temperature range is 10.3\u003csup\u003eo\u003c/sup\u003e Celsius to 36.5\u003csup\u003eo\u003c/sup\u003e Celsius. Topographically the district is composed of hills and plateaus, soils characterized as Lithosol- Cambisols, whereas in the valleys, soils are classified as Fluvisol- Vertisols (GRZ, 1986). The elevation varies from 450-1000m at the Luangwa River valley bottom and extends to the plateau near Nyimba district center, and even higher on the mountain tops in the district\u0026rsquo;s western part.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3. Vegetation type\u003c/h2\u003e \u003cp\u003eThe Miombo woodland is the most dominant formation and habitat type in Southern Africa (Gumbo and Dumas-Johansen, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Montfort et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Miombo woodland is also the major forest type in Zambia itself, covering approximately 45% of the entire land surface (Kalinda, 2008). Nyimba is located in the middle of the Miombo Ecoregion, a biome with a variety of flora types that is dominated by tree species from the Caesalpinioiae subfamily of leguminous plants (Timberlake and Chidumayo, 2011). Depending on the climate, soil, landscape position, and degree of disturbance, the ecoregion's vegetation varies in composition and structure(Timberlake and Chidumayo, 2011; Halperin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Nyimba is located in the arid ecozone and is characterised by four types of plants: Dry miombo woodland (\u003cem\u003ei.e., Brachystegia spiciformis\u003c/em\u003e, B. boehmii, and \u003cem\u003eJulbernardia globiflora\u003c/em\u003e), Mopane woodland (\u003cem\u003ei.e., Colophospermum mopan\u003c/em\u003ee), Munga woodland (\u003cem\u003ei.e.\u003c/em\u003e, Vechellia sp., Senegalia sp., Combretum sp., and trees associated with the Papilionoideae subfamily) and Riparian Forest (\u003cem\u003ei.e.\u003c/em\u003e, mixed tree species).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4. Land use and farming systems\u003c/h2\u003e \u003cp\u003eNyimba district's total land area is about 10,500 square kilometers according to the population and housing census of 2010. Therefore, 82% of the district population is agrarian with average household income. These households are farmers who are into mixed agriculture practices dominating the agricultural scene in the district. However, local smallholder farmers in the district practice some sort of shifting cultivation. Under this agricultural system, crops are grown in mounds and/or ridges in most cases maize. The major crops grown include banana (Musa sp.), maize (\u003cem\u003eZea mays\u003c/em\u003e), finger millet (\u003cem\u003eEleusine coracana\u003c/em\u003e), groundnuts (\u003cem\u003eArachis hypogaea\u003c/em\u003e), haricot bean \u003cem\u003e(Phaseolus vulgaris)\u003c/em\u003e, cowpeas (\u003cem\u003eVigna unguiculata\u003c/em\u003e spp.) and soybean (\u003cem\u003eGlycine max\u003c/em\u003e). Multiple cropping systems are common among farming households where cultivated land is on gently and moderately steep slopes. The topography of the land in the district makes the agricultural or cultivation pattern different from other areas. Therein, the cropping system is alongside livestock production such as cattle, goats, chickens, ducks, and doves. Besides agricultural activities, farmers are engaged in charcoal production, timber, firewood supply, and non-timber forest products (NTFPs) from the miombo woodland for household economic gain (\u003cem\u003ePolicy Brief\u003c/em\u003e, 2016).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Site Selection\u003c/h2\u003e \u003cp\u003eAn exploratory survey was conducted to collect basic information about the study area before actual data collection. Information gathered included; distance between villages, number of farming households per village, contact details for lead farmers, CSA practices adopters\u0026rsquo; households and the location of croplands, and identifying central meeting points for focus group discussion (FGD).\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Sampling technique\u003c/h2\u003e \u003cp\u003eThis research used a multistage random sampling technique to select participants to be part of the study. This study drew smallholder farmers from agricultural camps. An agricultural camp is a delineation made by the Republic of Zambia Ministry of Agriculture containing a certain number of smallholder farmers' households in a district across villages for easy access by agriculture extension officers. From the eight (8) agricultural camps in Nyimba District, four (4) agricultural camps were randomly selected (\u003cem\u003ei.e.\u003c/em\u003e, Ndake, Central camp, Lwende, and Ofumaya). The total number of farmers in the selected four (4) agricultural camps in Nyimba District is 10,700. The study made use of Slovin\u0026rsquo;s formula for sample size calculation. Further, the study randomly selected three (3) villages (\u003cem\u003ei.e.\u003c/em\u003e, Sikwenda, Sichipale, Mawanda, Elina, Katumbila, Sichalika, Malalo, Mwenecisango, Mulivi, Lengwe, Mofu and Yona) from each camp. The study first used a margin of error of 0.05 and obtained a sample size of 386 participants. However, this sample size required more time and resources, to reduce the sample size, the study then used a margin of error of 0.1 and obtained a size of 99, as shown below.\u003c/p\u003e \u003cp\u003eSample size formula: Slovin\u0026rsquo;s (1960) formula.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$n=\\frac{N}{1+N{e}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$n=10700/(1+10700\\left({0.1}^{2}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$n=10700/27.75 n=99.07$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe study therefore settled for a sample size of 194 participants, which is between the sample size of 99 (0.1 margin of error) and 386 (0.05 margin of error). Through the aid of agricultural camp officers, farmer registers for each village were used to randomly select participants in an Excel spreadsheet.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Focus group discussion\u003c/h2\u003e \u003cp\u003eFocused group discussions (FGD) were conducted to collect in-depth data about smallholder farmers\u0026rsquo; factors affecting climate-smart agriculture practices (CSAP) adoption, crop productivity, perceptions on climate-smart agricultural practices, CSAP practiced as well and perceptions on climate change. This was attained via means of a developed open-ended FGD study tool. The FGDs are regarded to be better than individual interviews as sensitive issues come out during the execution. Individual farmers tend to fail to express themselves fully during a one-on-one interview. A total of four (4) FGDs were carried out in the study area comprising village headmen, women, men, and youths. The FGD meetings were held at central places for easy access by individual farmers. The core purpose of FGDs was to supplement collected data via household questionnaires.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Household interviews\u003c/h2\u003e \u003cp\u003eThe questionnaire included closed and open-ended questions. Before carrying out the household interviews, the questionnaire was pre-tested six (6) times for appropriateness (e.g., clarity, adequacy, and question sequence), and then changed based on the results. Smallholder farmers\u0026rsquo; households not participating in the survey were used in pre-testing the questionnaires. The study engaged three Enumerators who were taught and overseen by the principal researcher, with experience in data collection. Collected data was verified and amended after each fieldwork day and backed on CSPRO Cloud.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Variables specification\u003c/h2\u003e \u003cp\u003e \u003cb\u003eOutcome Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe outcome variable for this study is the impact of CSAP adoption among smallholder farmers' households' crop productivity. And factors influencing crop productivity and CSAP adoption among smallholder farmers in Nyimba district.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDependent Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSmallholder farmers\u0026rsquo; household decision to adopt CSAPs\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe dependent variable was the smallholder farmers\u0026rsquo; household to adopt CSAPs taking a value of one (1) and zero (0) if the smallholder farmers\u0026rsquo; household does not adopt. The main reason was to identify elements that influence the adoption of CSAPs among smallholder farmers\u0026rsquo; households in the Nyimba district, Zambia.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndependent Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eVariable description, measurements, and expected sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAGEHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSEXHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy (1 if male, 0 if female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDUHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe educational level of household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Number of years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHHSZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Adult equivalent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFEXCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtension services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy (1 if yes, 0 if no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccess to credit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy (1 if yes, 0 if no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal livestock unit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (TLU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold annual income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDummy (1 if yes, 0 if no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFERT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFertilizer use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmland size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Hectares)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClimate information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (1 if yes, 0 if no)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXPFRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarming experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContinuous (Years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Propensity score matching\u003c/h2\u003e \u003cp\u003ePropensity score matching (PSM) method was used in this study to determine the effect of CSAPs on crop productivity among adopters and non-adopters. Propensity score matching is a way of correcting treatment effect estimates by adjusting for confounding variables across a sampled population. According to Caliendo and Kopeinig (2008), there are steps in implementing PSM for a study. These are an estimation of the propensity scores using a binary model, choosing a matching algorithm, checking on common support conditions, and testing the matching quality of the treatment and/or participants.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep 1\u003c/strong\u003e \u003cp\u003eModel Specification\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe Logit model in this research, is preferred due to the consistency of parameter estimation associated with the assumption that the error term in the equation has a logistic distribution (Baker, 2000; Ravallion, 2001). Therefore, the Logit model was used to estimate the probability of smallholder farmers\u0026rsquo; adoption of CSAPs allotted to socio-economic, agroecological, and institutional characteristics. Therein, a dependent variable considered a value of 1 for CSAP adoption and 0 for non-CSAP adopters.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${P}_{i}=P\\left(Y=1|X\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn line with Pindyck and Rubinfeld (1981), the cumulative logistic probability function is specified as follows;\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${P}_{i}=F\\left({Z}_{i}\\right)=F\\left[a\\right.+\\sum _{i=1}^{m}{\\beta }_{i}{X}_{i}]=\\left[\\frac{1}{1+{e}^{-(a+\\sum {\\beta }_{i}{X}_{i}}}\\right]$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ee\u003c/em\u003e represents the base of natural logs, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e represents the i\u003csup\u003eth\u003c/sup\u003e explanatory variable, P\u003csub\u003ei\u003c/sub\u003e is the probability that a household adopted CSAP, and α and \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e are parameters to be estimated.\u003c/p\u003e \u003cp\u003eInterpretation of coefficients is made easier if the logistic model can be written in terms of the odds and log of odds (Gujarati, 1995). The odds ratio implies the ratio of the probability that an individual will be a participant (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) to the probability that he/she will not be a participant (1-P\u003csub\u003ei\u003c/sub\u003e). The probability that he/she will not be a participant is defined by:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\left(1-{P}_{i}\\right)=\\frac{1}{1+ {e}^{zi}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\left(\\frac{{P}_{i}}{1+ {P}_{i}}\\right)=\\left[\\frac{1+{e}^{zi}}{1+ {e}^{-zi}}\\right]={e}^{zi}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAlternatively,\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\left(\\frac{{P}_{i}}{1+ {P}_{i}}\\right)=\\left[\\frac{1+{e}^{zi}}{1+{e}^{-zi}}\\right]={e}^{\\left[a+ \\sum {B}_{i}{X}_{i}\\right]}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTaking the natural logarithms of Eq.\u0026nbsp;(3.5) will give the logit model as indicated below.\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${Z}_{i}=ln\\left(\\frac{{P}_{i}}{1-{P}_{i}}\\right)=a+{B}_{1}{X}_{1i}+{B}_{2}{X}_{2i}+\\dots {B}_{m}{X}_{mi}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eI consider a disturbance term, \u0026micro;\u003csub\u003ei\u003c/sub\u003e, and the logit model becomes\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${Z}_{i}=a+{\\sum }_{t=1}^{m}{B}_{t}{X}_{ti}+{\\mu }_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eSo the binary logit will become:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$Pr\\left(pp\\right)=f\\left(X\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003epp\u003c/em\u003e is CSAPs adoption, \u003cem\u003ef(X)\u003c/em\u003e is the dependent variable project participation and \u003cem\u003eX\u003c/em\u003e is a vector of observable covariates of the households. The dependent variable will take a value of 1 for CSAP adoption and 0 for non-adopters.\u003c/p\u003e \u003cp\u003eIn addition to the estimated coefficients, the marginal effects of the change in the explanatory variables on the probability of CSAP adoption are also estimated. The interpretation of these marginal values will be dependent on the unit of measurement for explanatory variables. However, when the explanatory variable is a dummy, the marginal effects generally produce a reasonable approximation to the change in the probability that \u003cem\u003eY\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, at a point such as the regressors' average.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep 2\u003c/strong\u003e \u003cp\u003eDefining the Region of Common Support and Balancing Tests\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe region of common support needs to be defined where distributions of the propensity score for treatment and comparison group overlap. Sampling bias may still occur, however, if the dropped CSAP non-adopters\u0026rsquo; observations are systematically different in terms of observed characteristics from the retained non-adopters; these differences should be monitored carefully to help interpret the treatment effect. Balancing tests can also be conducted to check whether, within each quantile of the distribution of propensity scores, the average propensity score and mean of \u003cem\u003eX\u003c/em\u003e are the same. For PSM to work, the comparison and treatment groups must be balanced in that similar propensity scores are based on similar observed \u003cem\u003eX\u003c/em\u003e. The distributions of the treated group and the comparator must be similar, which is what balance mplies. Formally, one needs to check if \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{\u0026circ;}\\text{P}\\left(X|T=1\\right)= \\text{\u0026circ;}\\text{P}\\left(X|T=0\\right)\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep 3\u003c/strong\u003e \u003cp\u003eMatching Adopters to Non-adopters\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe third step is to choose an algorithm for data matching available. Matching is a common method for deciding on control subjects who are matched to the treated subjects based on context covariates that the investigator believes need to be monitored. Different ones may employ matching standards. to assign adopters to non-adopters based on propensity score. The most common matching algorithms are nearest neighbor matching (NN), radius matching (RM), and kernel-based matching (KBM).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep 4\u003c/strong\u003e \u003cp\u003eMatching Quality\u003c/p\u003e \u003c/p\u003e \u003cp\u003eIn the fourth step matching quality tests could be done. Checking for matching regardless of quality the matching method can balance the distribution of various variables or not. If differences exist, there may be an indication of incomplete matching, and remedial actions are suggested (Caliendo\u0026amp; Kopeinig, 2008). The following step is to check whether the treatment introduced a distinction in the indicators of impact. The average treatment effect at the treated (ATT) is given by the distinction within the mean outcome of matched adopters and nonadopters that have common support conditional at the propensity score.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStep 5\u003c/strong\u003e \u003cp\u003eSensitivity Analysis\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFinally, a sensitivity analysis will be carried out to check the conditional independence assumption strength. Sensitivity analysis also will be utilized to look at whether an unmeasured variable's effect on the choice process is strong enough to jeopardize the matching approach (Ali \u0026amp; Abdulai, 2010). The Rosenbaum bound sensitivity test will be used to carry out the sensitivity analysis (r-bounds test).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Ethical Clearance\u003c/h2\u003e \u003cp\u003eThe study was solely conducted by the researcher and two supervisors with integrity, objectivity, openness, respect for research participants, respect for intellectual property, confidentiality, informed consent, fidelity, and honesty. The researcher and two supervisors take responsibility for any actions, and publications, and only make agreements intended for keeps. However, the study was approved by the University of Zambia Directorate of Research and Graduate Studies (NASREC) and bears a NASREC IRB No. 00005465 (\u003cb\u003eIORG No. 0005376)\u003c/b\u003e. The principal researcher did not intentionally engage in or participate in any form of malicious harm to research participants\u0026rsquo; personal information as well as breaching the University of Zambia research principles and research ethics for Haramaya University.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Effect of Climate-Smart Practices on Crop Productivity among Smallholder Farmers, in Nyimba, Zambia\u003c/h2\u003e \u003cp\u003eThe household survey comprised 194 smallholder farmer participants, from the research area, who were chosen at random. The smallholder farmers were interviewed about crop production and their applications of various CSA practices. The study presents the household survey's findings, starting with the demographic characteristics of the participants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), crop production and productivity, adoption of CSA, constraints on the adoption of CSA practices, effects of CSA practices on crop productivity, and factors affecting crop productivity. The study obtained a total of 339 field plots of various crops from the 194 farmer participants.\u003c/p\u003e \u003cp\u003eFrom the results in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the study obtained that the mean age for the respondents was 46 years of age, with a standard deviation of 14.59. The majority (62.18%) were male-headed households, and 69.43% were married. The mean years of formal education was found to be 5.49 years, with a standard deviation of 3.5. The mean years of farming was found to be 26.22, with a standard deviation of 15.55. Concerning the years of living in the areas, the mean was 30.92, and the standard deviation was 18.68. The average family size was 5.42, with a standard deviation of 2.14. The average total annual income was revealed to be K5472.68 (USD 331.68) (K16.5 per 1 USD), and the standard deviation of 7626.52. And 57.51% reported participating in any off-farm activities. While 78.76% of the smallholder farmer participants reported using improved seed varieties for farming, and the average farm size (landholding) was 3.396 ha, with a standard deviation of 3.363. The land tenure system was all customary land (100%). The mean cultivated land was 1.83 ha and 1.45 standard deviation. The average number of crops grown by smallholder farmers was 2, with a standard deviation of 0.930.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the participant smallholder farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH Head Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHH Head Sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMale: 62.18% (120)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMarried: 69.43% (134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of formal education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.499\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of farming\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of living in the area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Annual Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5472.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7626.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in any off-farm activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes: 57.51% (111)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed Improved Maize Seed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYes: 78.76% (152)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm Size (ha)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand tenure system (Customary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e100% (194)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCultivated land (2021/2022), ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.448\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Crops (2021/2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWith regards to the crops grown by the farmers, the study found that maize was the most grown crop, reported in 194 crop plots, followed by Groundnuts, reported in 99 plots, then sunflower in 69 plots, and soya beans in 16 plots (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The other crops; Cowpea, Bambara nuts, Cotton, Millet, and Sweet Potatoes were reported to have been grown in a few plots.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCrops grown by smallholder farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrops Grown\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCumulative\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybeans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e54.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundnuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCowpea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBambara nuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCotton\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSweet potatoes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMillet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e387\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the results obtained, pot-holing (basin) was implemented in 61 field plots (17.99%), multi-cropping in 50 plots (14.75%), minimum tillage in 34 plots (10.03%), ripping in 32 plots (9.44%), crop rotation in 18 plots (5.31%), and manure in 11 plots (3.24%) as well as alley cropping in 9 plots (2.65%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The other CSA practices were implemented in a few plots less than ten.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate-smart agriculture practices adopted by smallholder farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA Practices\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRipping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop rotation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrop residue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlley cropping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulti cropping\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eContour ploughing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompost\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManure field\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZero tillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBunding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcerning the number of CSA adopted, no single CSA practice was implemented in 167 plots (49.26%), one CSA practice was implemented in 123 plots (36.28%), two CSA practices were implemented in 43 plots (12.68%), 4 plots had three different CSA practices implemented, and only 1 plot had four CSA practices implemented and another plot with five CSA practices implemented (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Based on these results, farmers\u0026rsquo; implementation of many CSA practices in a single plot was found to be very low.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of climate-smart agriculture practices adopted by smallholder farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo._CSA_Adopted/Plot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFreq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCum.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e98.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e339\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the study results below (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), Maize, Groundnuts, Sunflower, and Soya beans were the most grown crops by the farmers. The mean quantity of harvest for all crops was 1223.51 Kg with a standard deviation of 1442.82. The mean quantity of maize harvested for maize was 1766.57 Kg with a standard deviation of 1594.23, while the mean quantity of Groundnuts harvested was 511.08 Kg with a standard deviation of 605.07, a mean quantity of 609.67 Kg with a standard deviation of 513.02 for sunflower, while for soya beans the mean quantity harvested was 1007.5 Kg with standard deviation of 1835.615 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuantities harvested for various crops (kg)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1223.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1442.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1766.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1594.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundnuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e511.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e605.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e609.6721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e513.0212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoya beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1007.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1835.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcerning the productivity of various crops, the overall yield per hectare of all the crops was 1316.60 kg with a standard deviation of 1214.13 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), while the yield per hectare for maize was found to be at 1682.52 kg per hectare with a standard deviation of 1325.87, and for groundnuts, the mean yield per hectare was found to be 822.90 kg with a standard deviation of 547.88, for sunflower, the mean yield was 962.79 kg with a standard deviation of 437.38, and for soya beans, the mean yield per hectare was 808.40 kg, with a standard deviation of 426.74.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eProductivity of various crops (Yield (Kg) per hectare)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYield per hectare (Kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Crops\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1316.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1214.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11630.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1682.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1325.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e119.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11630.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroundnuts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e822.9003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e547.8818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.6667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunflower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e962.7869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e437.3807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoya beans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e808.4048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e426.7391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1740\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study investigated how climate-smart agriculture techniques affected smallholder farmers' crop yield. The study found that crop yield for CSA adopters was 20.20% higher than for CSA non-adopters (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The results were statistically significant at 0.027 p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This entails that adopting CSA practices increases crop yield.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of climate-smart Practices on Crop Productivity among Smallholder Farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTreatment-effects estimation Number of Obs\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEstimator: propensity-score matching Matches: requested = 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eOutcome model: matching min\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTreatment model: logit max = 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog_yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI Robust Std. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Adopters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon_Adopters)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.2019652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0911943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.0232276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.3807028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance codes: ***\u0026lt;1%, **\u0026lt;5% and *\u0026lt;10%; Author\u0026rsquo;s calculation using Stata 15MP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study conducted a propensity score matching analysis to specifically find out how the CSA affects maize productivity (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The research showed that implementing CSA increases maize yield for adopters by 21.50% higher than the non-adopters. This shows that adopting CSA practices significantly increases maize yield. The results were statistically significant at 0.035 p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImpact of climate-smart Practices on maize productivity among smallholder farmers\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTreatment-effects estimation Number of Obs\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eEstimator: propensity-score matching Matches: requested = 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eOutcome model: matching min = 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTreatment model: logit max = 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog_yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI Robust Std. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Adopters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003evs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon_Adopters)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.215012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.101795\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.035**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.015496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.414527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance codes: ***\u0026lt;1%, **\u0026lt;5% and *\u0026lt;10%; Authors\u0026rsquo;calculation using Stata 15MP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study conducted a logistic regression analysis to determine factors affecting the adoption of CSA practices. According to the study, age has a favorable impact on the adoption of CSA practices, the higher the age, the more likely a farmer will adopt CSA practices, statistically significant at 0.0000 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The study recorded the age category of 40\u0026ndash;55 years and \u0026gt;\u0026thinsp;55 years to have adopted more CSAPs in the study area. A study by Saha et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), from the logit model indicated that a farmer's level of education, occupation, family size, cultivated farm size, farmers choice of adaptation techniques for climate change is influenced by their level of agricultural experience, ownership of cattle, annual income, market difficulty, access to farm information, training background, affiliation with organizations, and perception of climate change. The study discovered that adopting CSA practices is influenced by previous farming experience, the more years a farmer spends in farming, the less likely a farmer will use CSA practices, statistically significant at 0.0000 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Income was found to have a statistically positive effect on the adoption of CSA practices, the greater a farmer's income level, a farmer is more likely to adopt CSA practices, statistically significant at 0.0640 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). Zakaria et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also demonstrated that the intensity of farmers\u0026rsquo; adoption of climate-smart agricultural technologies is positively influenced by farmers\u0026rsquo; experience in rice cultivation, access to mass media, training, and perceived decrease in the quantity of rain. On the other hand, the size of the farm, the distance between the farmers' homes and the farm sites, the location, and the reported rise in temperature all hurt the farmers' intensity of technology adoption.\u003c/p\u003e \u003cp\u003eGender in this study was found statistically significant at 0.0660 p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Farm size was also found to have a negative significant effect on climate-smart agricultural practices adoption at 0.0050 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Livestock quantity was also found to have a significant effect on climate-smart agriculture adoption at 0.0180 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1) whilst access to climate information had a negative influence on climate-smart agriculture adoption p-value 0.0060 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A study by Kurgat et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that female ownership of farm assets, farm location, and household resources were major determinants of climate-smart agricultural adoption in Tanzania. Aryal et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) concluded that several factors, such as household characteristics, market access, and main climate hazards are found to affect the probability and level of implementing different climate-smart practices of climate-smart agricultural adoption by smallholder farmers. A similar study by Abegunde et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) On the other hand; marital status, education, fertilizer, credit access, and access to extension services were found not to have a significant effect on the adoption of CSA practices.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors affecting smallholder farmers\u0026rsquo; adoption of climate-smart agricultural practices\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLogistic regression Number of Obs\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eWald chi2(10) = 27.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2 = 0.0112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLog pSeudolikelihood = -204.0124 Pseudo R2 = 0.0965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust Std. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.085697***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.017260*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.4056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital_status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.178756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.2800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.4530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.051048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.3200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming_experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.087116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.3600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold_size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.027906\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000035*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFertilizer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarm_size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.02006**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLivestockqt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006734*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredit_access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.150782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.6300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.6221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess_to_climate_inform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.44108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.6014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension_services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e_cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.416121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.4200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.3792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance codes: ***\u0026lt;1%, **\u0026lt;5% and *\u0026lt;10%; Author\u0026rsquo;s calculation using Stata 15MP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study carried out Cobb Douglas production analysis to determine factors affecting the productivity of crops (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). Study results showed that income has a positive significant impact on crop productivity, productivity improves by 0.002% the outcome of farmers' increase in income level, statistically significant at 0.0040 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A study by Urgessa, (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) showed that the most important factors that influence agricultural labor are determined to be the land-labor ratio, fertilizer and pesticide use, manure use, and household size. and land productivity (\u003cem\u003ei.e.\u003c/em\u003e crop productivity). According to WenJing et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) study of income differences across households\u0026rsquo; quartiles, the study found households with high on-farm income are more sensitive about enlarging their farm size by renting farmland, and households with middle and upper-middle off-income may benefit more from renting out their farmland. Fertilizer was found to have a significant positive impact on crop productivity. A unit increase in fertilizer use was associated with a 0.12% increase in crop yield, statistically significant at 0.0000 p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Du et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicated long synthetic fertilizer and manure application have benefits on crops produced and soil production. Farm size was found to harm crop productivity, the bigger the farm size, the lower the crop productivity by 6.52%. Livestock quantity was found to have a positive significant influence on crop productivity, the higher the number of livestock a farmer has, the higher the yield of crops by 0.86%, statistically significant at 0.0001 p-value (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Livestock provides farming households with manure and animal draught power to produce crops and the investment of income from livestock into technologies that benefit crop production (Anderson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). In addition, to the effects of manure and draught on crop output; money from livestock is frequently invested in terms that improve crop production. Adopting CSA practices was found to have a profoundly favorable effect on crop productivity, if one more farmer adopts CSA practices, the average yield for the farmers improves by 13.49%, statistically significant at 0.0720 p-values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). The other factors were found not to have a significant impact on crop yield. Mujeyi et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found similar results on the adoption of climate-smart agriculture to significantly contribute to the crop yield of smallholder farmers on an integrated crop-livestock system. Marital status influences crop productivity by 0.07% whilst the education level of the household head had a 0.098% influence on contribution crop productivity among smallholder farmers. Household size also contributed 0.2% to smallholder farmers crop productivity. A similar study by Serote et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) according to the findings, smallholder farmers' household demographics characteristics and institution characteristics influenced the adoption of climate-smart agriculture and crop productivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactors affecting smallholder farmers\u0026rsquo; crop productivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLinear regression Number of Obs\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eF(9, 179) = 11.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F = 0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eR-squared = 0.6441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRoot MSE = 0.74495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elog_yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRobust Std. Err.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[95% Conf.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInterval]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.3700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital_status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00755*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0670\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00980*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarming_experie\u0026thinsp;~\u0026thinsp;e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0140\u003c/p\u003e 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align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00863**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSA_Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.13490*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCredit_access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.11707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.5800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess_to_climate Inform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.5408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtension_services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.5100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.1236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.2094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e__cons\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.3095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.5845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.8026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSignificance codes: ***\u0026lt;1%, **\u0026lt;5% and *\u0026lt;10%; Author\u0026rsquo;s calculation using Stata 15MP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Climate-smart practices impact on crop productivity\u003c/h2\u003e \u003cp\u003eThe objective of the study was to determine the impact of CSA practices adoption on crop productivity among smallholder farmers in Nyimba district, Zambia. The study also determined factors affecting crop and adoption of climate-smart practices (CSAPs) among smallholder farmers in Nyimba district, Zambia. Based on data analysis, the study found that smallholder farmers\u0026rsquo; crop yields of CSAP adopters were 20.20% higher than for non-adopters. The study also found that implementing CSAPs increases maize yield for smallholder farmers adopters by 21.50% higher than non-adopters. This clearly shows that adopting climate-smart agricultural practices significantly increases crop productivity for smallholder farmers.\u003c/p\u003e \u003cp\u003eSimilarly, Abegunde et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) carried out a study on the effect of climate-smart agriculture on household food security, with 327 smallholder farmers sampled through a multi-stage technique, and employed binary logistic and multinomial logistic regression models. The findings revealed that CSA practices adaption significantly and favorably affects household food security. The findings also indicated that agricultural revenue and income from non-farm sources had a significant and favorable impact on household food security (Abegunde et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMossie (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) also conducted a similar study on the impact of climate-smart agriculture technology on productivity in Southern Ethiopia. The study analyzed data using propensity score matching to determine the technology impact on adopters and non-adopters. According to the findings, wheat row planting has a favorable and meaningful effect on productivity. The study also found that smallholder farmers who sowed wheat in planting rows produced 1368 kg of wheat per hectare compared to the counterfactual scenario. Therefore, the CSA practice of row planting had a significant impact on yield (Mossie, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTadesse et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) conducted a study on the impact of climate-smart agriculture in Ethiopia on soil carbon, crop productivity, and fertility. The results showed that yield was 30\u0026ndash;45% higher under CSA practices than the control (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The pH of the soil was very minimally raised by CSA interventions, and the amount of total nitrogen and phosphorus that was available to plants increased by 2.2\u0026ndash;2.6 and 1.7\u0026ndash;2.7 times, respectively, in comparison to the control. The research shows that by increasing crop yield and minimizing nutrient depletion, CSA practices can help resource-poor farmers become more resilient to climate change. (Tadesse et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA study by Kichamu-Wachira et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) on the effects of climate-smart agricultural practices on crop yields, soil carbon, and nitrogen pools in Africa. The study revealed similar results that the implementation of CSAP significantly increased crop yields among smallholder farmers in Africa. The study also further concluded that CSAPs are an alternative advanced agricultural technology as compared to conventional farming typologies due to their enhancement of food production through climate mitigation and soil quality. Furthermore, Amadu et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) sampled 808 smallholder farmers' households in southern Malawi in the project area and found that 53% of CSAP adopters had increasing yields of maize in the drought year of 2016. Fentie and Beyene's (2019) research findings from the PSM model revealed that the adoption of CSAPs had a significant impact on crop yield per hectare. Therefore, scaling up CSA will significantly contribute to farmers' resilience to adverse effects of the changing climate and climate variations by enhancing crop productivity and food security among farming households. Beedy \u003cem\u003eet al\u003c/em\u003e.(2010) espoused the significant and positive influence of \u003cem\u003eGliricidia sepium\u003c/em\u003e Alley cropping on soil organic matter influence on a compiled single field of maize. Alley cropping had impacts on the changes in soil physicochemical properties enhanced maize yields, and increased soil nutrients over the mid and long term.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eClimate-smart agriculture holds a promise for humankind and the earth's planet, it can be successful if all developed and developing countries produce more food while generating less environmental pressure. According to the results of this study, adopting climate-smart farming practices can significantly improve the welfare of smallholder farmers. Consequently, the research recommends that many more interventions are warranted to ensure that impoverished and vulnerable smallholder farmers' households have access to improved agricultural technologies. Through better coordination of their separate actions, key stakeholders can use the study's findings to assist smallholder farmers\u0026rsquo; households in the study area and Zambia who are experiencing adoption issues.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApplicable through the University of Zambia Postgraduate Research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this research is available upon request. Furthermore, all the data that was reviewed for this paper have been published elsewhere. References including doi codes, as available, are availed accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFunding was provided by the World Bank through the African Centre of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University, Ethiopia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePetros Chavula: -MSc student conducted the study and wrote the manuscript\u003c/p\u003e\n\u003cp\u003eDr. Chizumba Shepande and Dr. Samuel Feyissa edited the manuscript whilst Dr. Million Sileshi did the proofreading of the manuscript.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbegunde, V. 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Adoption of Climate-Smart Agriculture Technologies in Tanzania. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(May). https://doi.org/10.3389/fsufs.2020.00055\u003c/li\u003e\n \u003cli\u003eMakate, C. (2019). \u003cem\u003eLocal institutions and indigenous knowledge in adoption and scaling of climate-smart agricultural innovations among sub-Saharan smallholder farmers\u003c/em\u003e. 270\u0026ndash;287. https://doi.org/10.1108/IJCCSM-07-2018-0055\u003c/li\u003e\n \u003cli\u003eMolieleng, L., Fourie, P., \u0026amp; Nwafor, I. (2021). Adoption of Climate Smart Agriculture by Communal Livestock Farmers in South Africa. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(18), 10468.\u003c/li\u003e\n \u003cli\u003eMontfort, F., Nourtier, M., Grinand, C., Maneau, S., Mercier, C., Roelens, J.-B., \u0026amp; Blanc, L. (2021). Regeneration capacities of woody species biodiversity and soil properties in Miombo woodland after slash-and-burn agriculture in Mozambique. \u003cem\u003eForest Ecology and Management\u003c/em\u003e, \u003cem\u003e488\u003c/em\u003e, 119039.\u003c/li\u003e\n \u003cli\u003eMossie, W. A. (2022). The Impact of Climate-Smart Agriculture Technology on Productivity: Does Row Planting Matter? Evidence from Southern Ethiopia. \u003cem\u003eThe Scientific World Journal\u003c/em\u003e, \u003cem\u003e2022\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMujeyi, A., Mudhara, M., \u0026amp; Mutenje, M. (2021). The impact of climate-smart agriculture on household welfare in smallholder integrated crop\u0026ndash;livestock farming systems: evidence from Zimbabwe. \u003cem\u003eAgriculture \u0026amp; Food Security\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(1), 1\u0026ndash;15.\u003c/li\u003e\n \u003cli\u003eMurray, V., \u0026amp; Ebi, K. L. (2012). IPCC special report on managing the risks of extreme events and disasters to advance climate change adaptation (SREX). In \u003cem\u003eJ Epidemiol Community Health\u003c/em\u003e (Vol. 66, Issue 9, pp. 759\u0026ndash;760). BMJ Publishing Group Ltd.\u003c/li\u003e\n \u003cli\u003eNewell, P., Taylor, O., Naess, L. O., Thompson, J., Mahmoud, H., Ndaki, P., Rurangwa, R., \u0026amp; Teshome, A. (2019). Climate-smart agriculture? Governing the sustainable development goals in Sub-Saharan Africa. \u003cem\u003eFrontiers in Sustainable Food Systems\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e, 55.\u003c/li\u003e\n \u003cli\u003eNgoma, H., Lupiya, P., Kabisa, M., \u0026amp; Hartley, F. (2021). Impacts of climate change on agriculture and household welfare in Zambia: an economy-wide analysis. \u003cem\u003eClimatic Change\u003c/em\u003e, \u003cem\u003e167\u003c/em\u003e(3), 1\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eOdubote, I. K., \u0026amp; Ajayi, O. C. (2020). Scaling Up climate-smart agricultural (CSA) solutions for smallholder Cereals and livestock farmers in Zambia. \u003cem\u003eHandbook of Climate Change Resilience\u003c/em\u003e, 1115\u0026ndash;1136.\u003c/li\u003e\n \u003cli\u003e\u003cem\u003ePolicy brief\u003c/em\u003e. (2016). \u003cem\u003eDecember\u003c/em\u003e, 1\u0026ndash;6.\u003c/li\u003e\n \u003cli\u003eSaha, M. K., Abdul, A., Biswas, A., Meandad, J., Ahmed, R., Prokash, J., \u0026amp; Sakib, F. M. (2019). Factors Affecting to Adoption of Climate-Smart Agriculture Practices by Coastal Farmers in Bangladesh. \u003cem\u003eAmerican Journal of Environment and Sustainable Development\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(4), 113\u0026ndash;121.\u003c/li\u003e\n \u003cli\u003eSerote, B., Mokgehle, S., Plooy, C. Du, Mpandeli, S., Nhamo, L., \u0026amp; Senyolo, G. (2021). 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The Determinants of Agricultural Productivity and Rural Household Income in Ethiopia. \u003cem\u003eEthiopian Journal of Economics\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(2), 63\u0026ndash;91.\u003c/li\u003e\n \u003cli\u003eWenJing, H., ZhengFeng, Z., XiaoLing, Z., \u0026amp; Li, H. (2021). Farmland Rental Participation, Agricultural Productivity, and Household Income: Evidence From Rural China. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(9), 1\u0026ndash;22.\u003c/li\u003e\n \u003cli\u003eZakaria, A., Alhassan, S. I., Kuwornu, J. K. M., Azumah, S. B., \u0026amp; Derkyi, M. A. A. (2020). Factors Influencing the Adoption of Climate-Smart Agricultural Technologies Among Rice Farmers in Northern Ghana. \u003cem\u003eEarth Systems and Environment\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e(1), 257\u0026ndash;271. https://doi.org/10.1007/s41748-020-00146-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"African Center of Excellence for Climate-Smart Agriculture and Biodiversity Conservation, Haramaya University","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":"Adoption, Agriculture, Climate-smart agriculture, Climate change, Crop productivity","lastPublishedDoi":"10.21203/rs.3.rs-3604497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3604497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe environmental, economic, and social implications of climate change are anticipated to have a significant impact on smallholder farmers, whose way of life is heavily reliant on the environment. This study evaluates factors influencing the adoption of climate-smart agriculture practices and crop productivity among smallholder farmers in Nyimba District, Zambia. Data was collected from 194 smallholder farmers' households from June to July 2022 in twelve villages placed in four agricultural camps of Nyimba District. Four focus group discussions were also conducted to supplement data collected from the household interviews. A logistic regression model was used in this study to assess the determinants of crop production and the adoption of climate-smart agriculture in response to changes in climate and climate variations. Propensity score matching was also performed to assess the impacts of climate-smart agriculture adoption among adopters and non-adopter farming households' crop yields in the study area.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eResults from the study logit regression model indicate that the smallholder farmer\u0026rsquo;s level of education, household size, fertilizer usage, age of household head, gender, farming experience, livestock ownership, annual income, farm size, marital status of household head, and access to climate information, all affect smallholder farmers\u0026rsquo; household\u0026rsquo;s climate-smart agriculture practices adoption and crop productivity. The study propensity scores matching analysis found that crop yield for smallholder farmers\u0026rsquo; climate-smart agricultural practices adopters was 20.20% higher than for non-adopters. The analysis also found that implementing climate-smart agriculture practices in the study area increases maize yield for smallholder farmers adopters by 21.50% higher than non-adopters.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study provides direction for policymakers to strengthen farmers' adaptation strategies to climate change and guide policies through the adoption of climate-smart agricultural practices. However, these practices and efforts are capable of lessening the adverse effects of changes in climate and improving agriculture production.\u003c/p\u003e","manuscriptTitle":"Factors Influencing Climate-Smart Agriculture Practices Adoption and Crop Productivity among Smallholder Farmers in Nyimba District, Zambia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-14 17:10:45","doi":"10.21203/rs.3.rs-3604497/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":"9f7cdd59-271e-4358-b2e4-5ffb73259108","owner":[],"postedDate":"November 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":26261920,"name":"Agronomy"}],"tags":[],"updatedAt":"2023-11-14T17:10:45+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-14 17:10:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3604497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3604497","identity":"rs-3604497","version":["v1"]},"buildId":"pf3fE39SIOqb-0xH_OWvX","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}