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 < 0.001). The study recorded the age category of 40–55 years and > 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.