5.1 Determinants of choosing different combinations of CSA technologies
Table
3 presents the results of the MNL model estimates, revealing the factors influencing farmers’ decisions to select and adopt CSA technologies. We used non-adopters (D
0R
0Z
0) as the reference group in MNL estimations. Because the interpretation of the coefficients is not straightforward and it gives only the direction of the variables, the marginal effects of the MNL model are presented in Table
3 to provide a better understanding of factors influencing maize farmers’ decisions to adopt CSA technologies (Nguyen-Van et al.
2017). Our results show that the marginal effects differ greatly among the different CSA technology combinations.
Table 3
Determinants of adopting various combinations of CSA technologies: MNL model estimates
Age | 0.211 (0.573) | 0.610 (0.676) | − 0.018 (0.653) | 0.160** (0.068) | 0.977* (0.515) | 0.244*** (0.064) | 0.107** (0.052) |
Age squared | − 0.021 (0.028) | − 0.002 (0.004) | 0.040 (0.032) | − 0.003 (0.004) | − 0.005 (0.012) | − 0.010 (0.030) | − 0.012*** (0.003) |
Sex | − 0.197 (0.319) | − 0.567 (0.363) | − 0.477 (0.330) | 0.570 (0.497) | − 0.189 (0.282) | 0.503 (0.430) | − 0.131 (0.292) |
Household head | 0.337 (0.426) | 0.854 (0.585) | 0.477 (0.452) | 0.081 (0.572) | − 0.066 (0.332) | 0.366 (0.587) | − 0.346 (0.336) |
Education | − 0.074 (0.219) | − 0.117 (0.245) | − 0.307 (0.203) | 0.081 (0.307) | − 0.256 (0.176) | 0.336 (0.263) | 0.232*** (0.088) |
Marital status | − 0.043 (0.263) | 0.235 (0.319) | 0.305 (0.301) | − 0.015 (0.362) | 0.265 (0.246) | 0.689** (0.349) | 0.186 (0.251) |
Experience | − 0.222 (0.194) | − 0.570** (0.222) | 0.546** (0.231) | − 0.626*** (0.237) | 0.075 (0.175) | − 0.304 (0.226) | 0.212 (0.187) |
Household size | 0.034 (0.181) | 0.383* (0.217) | − 0.116 (0.193) | 0.183 (0.251) | 0.016 (0.159) | 0.659*** (0.191) | 0.387** (0.155) |
Farm size | 0.026 (0.158) | 0.238 (0.182) | − 0.340* (0.174) | 0.001 (0.207) | − 0.210 (0.139) | − 0.154 (0.172) | 0.470*** (0.138) |
Market information | 0.108*** (0.023) | 0.148*** (0.026) | 0.100*** (0.025) | 0.210*** (0.029) | 0.211*** (0.020) | 0.217*** (0.025) | 0.244*** (0.021) |
Market distance | 0.150 (0.133) | 0.234 (0.165) | − 0.484*** (0.124) | 0.236 (0.192) | − 0.551*** (0.106) | − 0.440*** (0.141) | − 0.534*** (0.109) |
Land ownership | 0.239 (0.184) | 0.064 (0.224) | 0.160 (0.197) | 0.378 (0.275) | 0.594*** (0.167) | 0.260 (0.227) | 0.462*** (0.174) |
Drought stress | 0.110*** (0.023) | − 0.825** (0.398) | 0.373 (0.251) | 0.131 (0.328) | 0.189*** (0.020) | 0.384 (0.281) | 0.153*** (0.020) |
Pest and disease stress | 0.302 (0.214) | − 0.029 (0.246) | − 0.577*** (0.213) | − 0.086 (0.281) | 0.716*** (0.176) | − 0.175 (0.243) | 0.564*** (0.183) |
Extension information | 0.152*** (0.021) | 0.240 (0.276) | − 0.090 (0.266) | 0.131*** (0.029) | − 0.051 (0.213) | 0.131 (0.272) | 0.061 (0.219) |
FBO membership | − 0.259 (0.189) | − 0.271 (0.224) | 0.341* (0.199) | 0.229 (0.260) | 0.484*** (0.163) | 0.638*** (0.222) | 0.285* (0.170) |
Radio information | 0.777*** (0.187) | − 0.919*** (0.229) | 0.679*** (0.199) | 0.833*** (0.259) | 0.554*** (0.164) | − 0.119*** (0.023) | 0.135*** (0.017) |
Colleague farmer information | 0.484** (0.203) | 0.108*** (0.027) | 0.282 (0.246) | 0.073 (0.270) | 0.679*** (0.210) | 0.163*** (0.033) | 0.175*** (0.024) |
Brong Ahafo | 0.788** (0.359) | 0.095 (0.329) | 0.142 (0.312) | 15.070 (482.667) | − 0.063 (0.253) | − 0.173 (0.328) | − 0.146 (0.265) |
Ashanti | 0.135*** (0.037) | − 0.180 (0.370) | − 0.237 (0.341) | 15.494 (482.667) | − 0.186 (0.275) | − 0.459 (0.374) | − 0.330 (0.289) |
Wald χ2 | 295.83*** | | | | | | |
LR χ2 (147) | 227.59*** | | | | | | |
Sample size | 3197 | | | | | | |
The results reveal that the farmer’s age has a positive and significant marginal effect (see columns 5, 6, 7, and 8 of Table
3). The findings imply that older farmers are more likely to integrate drought-resistant seeds and row planting (D
1R
1Z
0), drought-resistant seeds and zero tillage (D
1R
0Z
1), row planting and zero tillage (D
0R
1Z
1), and combinations of all the CSA technologies (D
1R
1Z
1). For example, the positive and significant marginal effect in column 5 of Table
3 suggests that older farmers are 16% more likely to integrate drought-resistant seeds and row planting (D
1R
1Z
0) than younger ones. The findings on age resonate with the results of previous studies (Vatsa et al.
2023; Zhou et al.
2023), opining that older farmers have more knowledge and experience, motivating them to adopt productivity-enhancing technologies. Also, the age square variable, which captures the long-term effect of age on CSA technology adoption, has a significant and negative coefficient. This implies that as farmers grow older beyond a certain age, their likelihood of adopting CSAs decreases. Older farmers are 1.2% less likely to adopt CSAs. This finding suggests that diminishing physical strength over time may contribute to the reduced adoption of CSA technologies among older farmers. This is consistent with the findings of Mossie (
2022) and Tanti et al. (
2022), who found an inverse relationship between the age square of farmers and the adoption of CSA technology.
Education positively and significantly affects all the combinations in the last column. The finding suggests that one 1-year increase in education would increase the probability of adopting drought-resistant seed, row planting, and zero tillage (D
1R
1Z
1) by 23.2%. Better education improves farmers’ awareness and understanding of the benefits associated with CSA technologies, increasing their adoption motivation. This finding resonates with the findings of Gebremariam and Wünscher (
2016) and Li et al. (
2024).
Married farmers are 68.9% more likely to incorporate row planting and zero tillage (D
0R
1Z
1) as CSA technologies. Married households have more members due to increased household size; hence, there is a desire to produce more food using CSA technologies. Makate et al. (
2019) reported similar findings. They indicated that married farmers are more likely to adopt CSA technologies, specifically to improve legume seeds, because marriage is an institution in Southern Africa. The results also show that an additional year spent on farming by a typical farmer would reduce the likelihood of adopting row planting alone (D
0R
1Z
0) by 57% and reduce the likelihood of incorporating drought-resistant seeds and zero tillage (D
1R
1Z
0) by 62.6%. Most experienced farmers are more conservative in their adoption of modern productivity-enhancing technologies. However, the experience variable positively affects adopting CSA technology, specifically zero tillage alone (D
0R
0Z
1). Experience is linked to training and information obtained on CSA technologies, as well as less drudgery and no/low cost of zero tillage, which could encourage farmers to adopt zero tillage technology easily.
The positive and significant effect of household size on all CSA technologies adoption (D
1R
1Z
1) suggests that a one-member increase in family size would increase the probability of adopting the three CSA technologies (D
1R
1Z
1) together by 38.7%. Bigger families generally signal greater resource endowment, such as labor, assisting farmers in crop cultivation and adopting CSA technologies, than smaller families. Previous studies have also found that household units positively influence the adoption of sustainable agricultural practices in Ghana (Setsoafia et al.
2022). An additional acre increase in farm size would increase the likelihood of adopting all three CSA technologies (D
1R
1Z
1) by 47%. Farmland is commonly described as a source of wealth, which motivates farm families, particularly those with large farms, to adopt CSA technologies. Anang and Amikuzuno (
2015) discovered similar results in northern Ghana. In addition, land ownership has a positive and significant effect on the adoption of drought-resistant seeds and zero tillage (D
1R
0Z
1), as well as all three CSA technologies (D
1R
1Z
1), which increases the adoption probability by 59.4% and 46.2%, respectively.
The distance from the farmer’s homestead to the nearest market positively influences the adoption of all three CSA technologies (D
1R
1Z
1). This finding indicates that the greater the distance farmers travel to the nearest agricultural market, the less probability of adopting CSA technologies. This is consistent with the findings of Anang and Amikuzuno (
2015), who asserted that longer market distances are associated with higher transportation and transaction costs, thus reducing the likelihood of adopting CSA practices. The variable representing the perceived pest and disease stress positively and significantly influences the adoption of all CSA technologies. Specifically, farmers who encountered an infestation of pests and diseases are more likely to adopt a blend of drought-resistant seeds and zero tillage (D
1R
0Z
1) and all three (D
1R
1Z
1). This is consistent with Teklewold et al. (
2013), who found that increased pest and disease stress increases the adoption of improved seeds in Ethiopia. In Ghana, Danso-Abbeam and Baiyegunhi (
2018) indicated that high-incidence pests and diseases encourage adopting pesticide management practices. However, this may not be the case everywhere, as our findings indicate that pest and disease stress negatively affect the adoption of zero tillage (D
0R
0Z
1), giving rise to the diverse influences of CSA technologies among Ghanaian smallholder farmers.
Farmers who receive CSA technology information via extension have a higher likelihood of adopting CSA technologies such as drought-resistant seeds (D
1R
0Z
0) alone and drought-resistant seeds and row planting (D
1R
1Z
0), which increases their adoption probability by 15.2% and 13.1%, respectively. Better extension services for farmers, such as training and education on climate-sustainable agricultural practices, tend to boost crop productivity, which may drive most farmers’ adoption of CSA technologies. Membership in farmer-based organizations has a positive and significant effect on the adoption of multiple CSA technologies, specifically incorporating drought-resistant seeds and zero tillage (D
1R
0Z
1), combining row planting and zero tillage (D
0R
1Z
1) and all three CSA technologies (D
1R
1Z
1). The findings confirm the extensive discussions about the advantages of farmer-based groups (see Manda et al.
2020a,
b; Yu et al.
2021; Zhang et al.
2020). For example, being a FBO member increases the desire to adopt improved seeds in Zambia (Manda et al.
2020a,
b) and soil and water conservation practices in Ghana (Amadu et al.
2020). Also, farmers who obtain information from a colleague are more likely to adopt multiple CSA technologies. Specifically, the likelihood of adopting a combination of drought-resistant seeds and zero tillage (D
1R
0Z
1), row planting and zero tillage (D
0R
1Z
1), and all three CSA technologies (D
1R
1Z
1) would increase by 67.9%, 16.3%, and 17.5%, respectively, if they received CSA technology information from colleagues.
The location dummies in columns 2, 4, and 5 are statistically significant. Our findings suggest that farmers in Ashanti and Brong-Ahafo are more likely to adopt only drought-resistant seeds (D1R0Z0) than farmers in the Northern region (reference group). The findings highlight the importance of including location control variables in model estimations by demonstrating how other socioeconomic conditions, resource endowments, climatic conditions, and institutional arrangements may influence smallholder farmers’ decisions to adopt CSA technologies.
5.2 Treatment effects of CSA technology adoption
Table
4 shows the treatment effects of CSA technology adoption on maize yields and net farm income. The results estimated from the second stage of the MESR model are not presented in the study for simplicity but are available upon request. The results show that relative to non-adoption (D
0R
0Z
0), adopting either row planting only (D
0R
1Z
0) or zero tillage only (D
0R
0Z
1) significantly reduces maize yields by 80 kg/acre and 94 kg/acre, respectively. One possible explanation for this phenomenon is that smallholder maize farmers in Ghana fail to adopt row planting and zero tillage appropriately, resulting in yield losses. Furthermore, the common practice of slash-and-burn agriculture among Ghana’s rural farmers can have a negative impact on soil performance, resulting in lower yields.
Table 4
ATT estimates of MESR model
D1R0Z0 | D0R0Z0 | 0.041 (0.028) | 0.054 (0.070) |
D0R1Z0 | D0R0Z0 | − 0.080*** (0.049) | 0.643*** (0.115) |
D0R0Z1 | D0R0Z0 | − 0.094** (0.039) | 0.143** (0.071) |
D1R1Z0 | D0R0Z0 | 0.208*** (0.066) | 0.677*** (0.134) |
D1R0Z1 | D0R0Z0 | 0.153*** (0.031) | 0.583*** (0.073) |
D0R1Z1 | D0R0Z0 | 0.098*** (0.036) | 2.078*** (0.161) |
D1R1Z1 | D0R0Z0 | 0.548*** (0.023) | 0.815*** (0.079) |
In comparison, adopting any two combinations of CSA technologies significantly increases maize yields. For example, relative to non-adoption (D0R0Z0), adopting drought-resistant seeds and row planting (D1R1Z0) together significantly increases maize yields by 208 kg/acre, and adopting drought-resistant seeds and zero tillage (D1R0Z1) significantly increases maize yields by 153 kg/acre. The yield effect is the largest when adopting all three technologies together. Specifically, relative to non-adoption (D0R0Z0), the adoption of all three technologies (D1R1Z1) significantly increases maize yields by 548 kg/acre.
The results that estimate the treatment effects of CSA technology adoption on net farm income are presented in the last column of Table
4. The results provide some interesting insights. When a single CSA technology is adopted, row planting has the largest impact on net farm income (D
0R
1Z
0). Relative to non-adoption (D
0R
0Z
0), adopting row planting (D
0R
1Z
0) significantly increases net farm income by 643 GHS/acre. When farmers combine two of the three CSA technologies, row planting and zero tillage (D0R1Z1) adoption have the largest impact of any CSA technology adoption option, increasing net farm income by 2078 GHS/acre. Relative to non-adoption (D
0R
0Z
0), adoption of all three CSA technologies (D
1R
1Z
1) significantly increases net farm income by 815 GHS/acre, and the impact is the second largest among all CSA technology options. Our findings corroborate with the recent findings (Amadu et al.
2020; Oduniyi and Chagwiza 2021; Setsoafia et al.
2022), highlighting that the adoption of multiple agricultural innovations has greater impacts on farm performance than the adoption of a single innovation.