1. Introduction

2. Literature Review

3. Materials and Methods

Empirical Models

The objective of this study was to analyse the impact of legume adoption on household welfare. We therefore start by specifying and estimate the model for determinants of legume adoption. To account for the intensity of adoption, the area under the legume production will be used as the dependent variable of determinants of the legume production.

Among the smallholder farmers, the production decisions for a chosen crop are thought to be a dependent variable determined by quite a number of factors (Gobena, 2012). Production decisions are generally done to maximize the expected returns which in turn is the function of the land allocation and the technology function which again is the function of the input and output prices (Verkaart et.al.,2017). The literature documents quite a number of factors that affects the adoption of the legume production among smallholder famers. Among the often-cited factors are the household landholding size, risk, human capital, labour availability, credit access, land tenure and access to input, output markets and the extension services (Feder, 1985; Tanti et.al., 2022). Additionally, the adoption decisions are also influenced by the agro-ecological and agronomic circumstances surrounding the small-scale farmers which may include: soil quality and type, rainfall patterns as well as the farming systems (Cavatassie et.al. 2010). Household’s endowments of natural, human, financial, physical and social capital constitute the resource constraints based on which wellbeing is maximized. Therefore, in addition to factors of production, our model of adoption includes household demographic characteristics such as household size and characteristics of the household head. We then estimated the determinants of legume adoption using random effects model and we specified the adoption model as follows:

$$Kit=\mathsf{\alpha }+\beta 1Xit+\beta 2Tit+\mathrm{Z}\mathrm{i}+Dt+\epsilon it$$

(1)

where $\mathsf{\alpha }$ is the constant

Where K_it is the area planted legumes by household i in year t

X_it is the vector of household demographic characteristics

T_it is a vector of institutional and ecological factors and

Z_i is the vector of time-invariant agronomic and individual characteristics both of which influence the preference of legume adoption to other crops.

ε_it is a compound error term consisting of unobserved time-invariant.

The study’s interest is to understand the welfare impacts of adopting the legume production. Therefore, to do this, we estimated the welfare impacts using the fixed effects model thereby extending our analysis by specifying equation (2) in our econometric model:

$$Iit=\alpha i+\beta 1Kit+\beta 2Tit+Dt+\epsilon it$$

(2)

I_i is the depended variable from the selected welfare indicators namely pericapta income, consumption and household asset holding. K_it is the area under legume production. All other variables are as described above.

To analyses the periodic impact of legume adoption over the years, we used the random effects model under the specification of the following model:

$$Ait=\alpha i+\beta 1Kit+\beta 2Tit+Dt+\mathrm{Z}\mathrm{i}+\epsilon it$$

(3)

Whereby A$it$_, $\alpha i,$ $\beta 1Kit$, $\beta 2Tit$,$\mathrm{Z}\mathrm{i},\epsilon i$t are as described above and $Dt$ is the dummy for the year 2016. “0” is for the year 2019 and “1” for 2016.

4. Results

Figure 2.  

Figure 2.  

4.2. Legume Adoption

The study firstly, focusses on identifying the determinants of legume adoption using double hurdle model which takes into account the intensity of adoption through considering land allocated to legume production. We therefore, considered land under legume an appropriate proxy for adoption. Although on average the landholding size among legume adopters is 0.49 acres higher than the landholding size among non-adopters (Table 3), there has been decrease in the land allocation to legume signifying lowering in the adoption rate. This again is explained by an ever increase in demand for the staple food to meet the food requirements for the growing population hence smallholder farmers have been shifting land from legumes to the other crops particularly maize (Brand,2011). The central and northern regions have both 39% rate of adoption while Southern region ranked behind with 19% (Figure 4). The results shows that over the years, Central region experienced an increase in the adoption rate. The results are different from the North as well as the Southern region which shows that during the period under analysis, the adoption rate decreased.

Figure 4. Trend of Legume adoption by rgion.

Figure 4. Trend of Legume adoption by rgion.

Figure 5. Percentage of adoption by region.

Figure 5. Percentage of adoption by region.

4.4. Econometric Results

4.4.1. Estimation of Legume Adoption

To identify the determinants of legume production we run random effects model specified in (eq.1). The disaggregated results for the adopters are as displayed in Table 6.

Table 4. legume adoption decision using double hurdle random effect.

Table 4. legume adoption decision using double hurdle random effect.

Variables	Coefficient	Standard Error	P-Values
Constant	2.777***	0.630	0.000
Soil Type	0.099*	0.045	0.027
Gender (Male=1)	-0.080	0.087	0.357
household size	-0.031	0.066	0.638
Annual - temperature(oC)	-0.007***	0.018	0.000
Rainfall(mm)	0.001**	0.0004	0.004
off-farm activity income	0.0003	0.0003	0.289
access to extension	0.149*	0.083	0.073
implement ownership	-0.080	0.156	0.605
landholding size(acres)	0.112**	0.044	0.012
land ownership (0=yes)	-0.076***	0.007	0.000
School attendance(1=yes)	-0.054	0.091	0.553
Age(head)	0.0004	0.003	0.898
Off farm activity dummy	-0.244*	0.132	0.065
Number of observations	496
R-squared	0.463***	0.776	0.000
***, **, *, mean significance at 1%, 5%, and 10% level, respectively

Overall, the model is statistically significant at 1% with 48% variations in the area under legume production explained by the explanatory variables. The test for the heteroscedasticity revealed the presence of the homoscedasticity. We transformed the variables using the natural log as a remedy to the violation of the assumption of the heteroskedasticity.

4.4.2. Ecological Factors Affecting Legume Adoption

4.4.2.1. Soil Type

The soil type is one of the ecological factors affecting the legume production. The results indicate that the soil type significantly affects the size of land allocated to a legume production. Disaggregating the results by the soil type indicated that there was 0.5 acres decrease in the legume adoption rate among the farmers whose their land soil is katondo, clay and sandy. The results are significant at 10%. This is in line with the Diatta et.al. (2019) findings which found that the legumes such as soyabeans and the groundnuts do better in the soils with the intermediate soil types such as loam clay or sandy loamy as compared to other soil types.

4.4.2.2. Mean Annual Temperature

Besides the soil type, the literature suggests that temperature is among the key ecological factors affecting the legume adoption in the SSA (Maggio et.al.,2021). The results indicates that significant at 1%, a degree’s Celsius increase in the temperature above the seal level significantly decreases adoption of the legume by 0.01 acre of land. This is because an increase in temperature reduces leaf area of legume plants which consequently affects the overall production hence farmers in the high temperature areas prefer growing other crops than legume.

4.4.2.3. Mean Annual Rainfall

One of the characteristics of the small-scale farming enterprise in Malawi is that the production decision is subject to the unimodal rainfall due to lower rate of irrigation technology adoption. It thus comes that the farmers decision to grow a particular crop is influenced by the rainfall amount their location receives. The results in Table 6. Indicate that a 1mm increase in the rainfall amount in the location of the households, increases the land allocation to the legume by 0.001 acre. The results are significant at 5% with a p-value of 0.003.

4.4.3. Institutional Factors affecting Legume Adoption

4.4.3.1. Access to Extension Services

The institutions factors such as access to extension service describe the context within which the farming takes place. Literature suggests that the access to extension services is one of the determinants of the legume adoption as it facilitates dissemination of information to farmers about the existence of the improved varieties that yield higher output. The results indicate that the access to extension services on the improved legume varieties increases the land area allocation to legume production by 0.15 acres as compared to those farmers who do not have an access to the extension services (Table 6).

4.4.4. Household’s Socioeconomic Factors Affecting Legume Adoption

4.4.4.1. Household Landholding Size

Land is the primary factor of production and stands out as major constraint to the level of farming practiced by the small-scale farmers in Malawi. literature suggest that the average landholding for the small-scale farmers in Malawi is 1.25 acres (0.5 ha) (NSO,2018) hence is one of the barriers to the legume adoption. The results in Table 6 reveals that significant at 5%, the increase in per capita household landholding size is accompanied by 0.12 acres increase in land allocation to the legume production.

4.4.4.2. Land Ownership

The ownership of land was found to have highest significant impact on the farmers decision to adopt legume production. For example, at 1% significant level, the ownership of land increases the household decision to allocate 0.08 acres land to legume production. This indicates that the land tenure plays a significant role in farmers decision making on their adoption of legume.

4.4.4.3. Off-Farm Activity Involvement

It is widely recognized that the capital is one other constraint that hinders progress among small holder farmers (Mapiye et.al.,2002). This is because the level of capital is the proxy for the access to the input such as improved legume seeds that are believed to have significant impact on the household welfare. The disaggregated results of household’s engagement in the other income generating activities indicates that the engagement in other livelihood activities increases the land allocation to the legume production by 0.24 acres. The results are significant at 10% and this indicates that the involvement in the other income generating activities increases the adoption of the legumes among small scale farmers in Malawi.

4.4.5. Legume Adoption impact on Household Asset Holding

The last two objectives of the study were to determine the welfare impact of the legume production among the legume adopters. The fixed effects was used to analyse the impact of the legume adoption on the household asset holding. Under the specification of the following model;

$$Ait=\alpha i+\beta 1Kit+\beta 2Tit+Dt+\epsilon it$$

(4)

where A_it is the total household asset holding which is the indicator for the welfare generated through linear combination process,

K_it is the area under legume production for household i at time t and all other variables are as specified in the methodology chapter.

We used the specified model above to fit the fixed effects model with the results presented in Table 7.

Table 5. Adoption impact on household asset holding: Fixed Effects Estimation.

Table 5. Adoption impact on household asset holding: Fixed Effects Estimation.

Variable	Parameter	Coefficient	Robust Std. Err.	p -Value
Constant	β0	53.33***	3.439	0.000
Gender (Male=1)	β1	1.038***	0.337	0.001
lnhousehold size	β2	-0.147**	0.066	0.027
Credit access(1=No)	β3	-0.321	0.346	0.353
education level Age(head) Off farm activity dummy Distance to the Road Religion Land size under legume	β4 β5 β7 β8 β9 β10	-0.341*** -0.019* 2.132*** 0.019 2.988*** 0.341*	0.346 0.010 0.533 0.017 0.522 0.196	0.000 0.051 0.000 0.257 0.000 0.082
Number of observations		1405
R-squared	β11	0.160***	3.604	0.000

***, **, *, mean significance at 1%, 5%, and 10% level, respectively.

The results above indicate that there is significant difference in the household’s asset holding between the non-adopters and adopters. The results are significant at 10% and reveal that a 1 acre increase in the land area allocated to the legume production, increase the house asset holding by 34%. This implies that the adopters were wealthier and have the better welfare than the non-adopters. This is because as proposed by Sen’s theory of entitlement approach in line with the DFID’s sustainable livelihood approach, assets are very important determinant of household welfare as they affect long-term household’s vulnerability to food insecurity and resistance to the external shocks. This is among the small-scale farmers, forms an important source of livelihood. Assets are also used as a coping mechanism in responding to external shocks. Disaggregating the results by landholding size shows that households’ largest landholdings experienced a 5.32 increase in the asset holding than the households with medium and the small landholdings.

4.4.6. Legume Adoption Impact on Household income

To Estimate the impact of legume adoption on the household income, we fitted the random effect model using the following model specification:

$$Yit=\alpha i+\beta 1Kit+\beta 2Tit+Dt+\epsilon it$$

(5)

Whereby $Yit$ is the household income measured in Malawian Kwacha with all other variables as described above. The results are presented in Table 8.

Table 6. Adoption impact on household income status: random effects estimation.

Table 6. Adoption impact on household income status: random effects estimation.

Variable	Parameter	Coefficient	Robust Std. Err.	p -value
Constant lnhousehold size	β0 β1	1.731*** 0.021	0.285 0.013	0.000 0.107
Gender (Male=1)	β2	0.235**	0.078	0.003
Age(head) education level Off farm activity dummy ReligionLand size under legume	β3 β4 β5 β6 β7	-0.001 -0.086*** 0.504*** 0.010 0.032*	0.002 0.016 0.111 0.064 0.020	0.528 0.000 0.000 0.120 0.096
Number of observations		1405
R-squared	β14	0.052***	3.604	0.000

***, **, *, mean significance at 1%, 5%, and 10% level, respectively.

Table 8 reveals the significant difference in peri capita income existing between the adopters and the non-adopters. The results show that the 1 acre increase in the area allocation to the legumes production by the small-scale farmers in Malawi, had the 3.3% increase in the income per capita among the adopting households. The results are consistent with the findings of Tegegne et. al. (2017) and Rahman et. al. (2020) which found that an increase in the area allocation to the legumes in Malawi is accompanied by significant increase in the household income.

4.4.7. Periodic Adoption Impact on Household Asset Holding

To assess the welfare changes over the years, the study used random effect model under specification of the following econometric model:

$$Ait=\alpha i+\beta 1Xit+Dit+\epsilon it$$

(6)

where A_it is the household asset holding for household i at time t, X_it is the vector of household demographic characteristics and $Dit$ is the dummy for a year 2016 with “1” for the year 2016 and “0” for 2019. The results are presented in the Table 9.

The results show that as compared to 2016, there was a significant increase in household asset holding in 2019. The one acre increases in the land allocation to the legume production in 2016 had a 71% decrease in the asset holding as compared to the 2019. This is because literature suggests years prior to 2019(2017 and 2018) legumes experienced significant increase in the price elasticisticity (3.88%) which increased the small-scale farmers income and consequently increased the asset holding (Dzanja et. al.,2019).

5. Discussion

6. Conclusions

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