This study was done as part of the DeSIRA (Developing Smart Innovations for Research in Agriculture) program funded by the European Union.
At an IFPRI virtual brown bag seminar held on the 24th of March, Malawi University of Science and Technology (MUST) Research Fellow, Tabitha C. Nindi, presented a study that explored why in spite of its reported benefits, there is a wide gap between the awareness and adoption of Sustainable Intensification practices (in particular, cereal-legume intercropping), amongst smallholder farmers in Malawi. The study set out to answer specifically how resource constraints (land and labor) and market access (input and output) can influence the farmer's adoption of SI, as well as how the farm households are influenced by yields and price risks.
A literature review suggests that the factors that affect the smallholder farmer’s decision include external factors such as policies, climatic conditions, and the market environment. Internal factors influencing their decisions, on the other hand, include biophysical conditions of the farm such as the topography, resource endowments, and also farmer-specific constraints such as level of skill, exposure, and access to information. The literature also suggests that with cereal-legume SI specifically, the factors are split between socioeconomic factors at the household level (such as land, labor, and capital), and policy-related factors (such as limited access to markets and information). For this study, the focus is on four factors: land labor, access to input and output markets.
The main crops of interest in this study were maize, beans, and pigeon peas and these were chosen because they are among the most commonly produced crops in Malawi. The pre-planting strategies considered were T1: pure stand cropping; and intercropping of T2: maize-beans, T3: maize-pigeon peas, T4: beans and pigeon peas, and T5: maize-beans-pigeon-peas. The study uses dynamic stochastic programming to evaluate how these constraints influenced the farmer’s production choices. This was the preferred method because it allows for a snapshot of farmers’ decision process under risk- and thus gives us the optimal production strategy that farmers would choose under risk. The model, therefore, compares the farmer's optimal production strategies across various scenarios which assess the impact of relaxing certain resource constraints and alternate policies (land, labor, and input and output markets). Scenario 1 has the status quo, which includes parameters based on the data on the average farmer in Malawi. Scenario 2 imposes changes on the status quo by relaxing the labor constraints (i.e., doubling the labor available). In Scenario 3, the land constraints are relaxed (increased by 20%) and in Scenario 4 it is assumed that the farmer has access to high-yielding legume varieties. In Scenario 5, it assumed that the farmer has access to high-value legume markets and in Scenario 6, all the above changes are imposed on the status quo.
The results showed that in Scenario 1, nearly all farmland is allocated to the pure maize stand, with very little going to intercropping beans and pigeon peas. In Scenario 2, the farmers’ optimal plan is to allocate more land to intercropping maize and beans and/or pigeon peas. Scenario 3 results showed that 40% of the land was allocated to triple cropping of maize, beans, and pigeon peas in the 1st and 2nd cropping cycles, possibly due to the land increase not being as substantial compared to the changes in other scenarios.
In Scenario 4, a legume cropping system is adopted with hardly any maize featuring the optimal solution. With Scenario 5, close to over 50% of the total farmland is allocated to double the cropping of legumes possibly because the farmers are able to realize higher prices. In Scenario 6, the farmer allocates all the land in year 1 to maize and then downscales to intercropping beans-maize, beans-pigeon peas, and maize-beans-pigeon peas in years 2 and 3. An additional Scenario 7 is considered to evaluate the impact of using improved storage technology- hermetic storage (PICS) bags, which seems to indicate that farmers stored a higher proportion of their harvest compared to the baseline scenario. For example, with the maize harvest, only 10 to 30 % of it is stored with ordinary woven bags but this increases to 22 to 73 % with PICS bags. The researcher proposes that without access to improved storage technology, the farmer may not engage in much storage of grain as a way to minimize post-harvest losses.
To conclude, the study affirms that this model’s results show the impact of the resource (land and labor) and market (input or output markets) constraints on farmers' cropping system choice. Specifically, it shows that 1.) Relaxing labor constraints by doubling the farm household’s labor endowments pushes the farmer towards more labor-intensive production systems involving intercropping of maize with beans and/or pigeon peas; 2.) Increasing access to high-yielding varieties and high-value markets also shows that the farm household will move towards more legume-based cropping patterns, and 3.) The lack of effective storage technologies may be discouraging farmers from participating in grain storage in the post-harvest period.
The study also suggests future policy implications such that there is a strong need for policies that could help increase access to high-yielding varieties and high-value markets for smallholder farmers. There may also be a need to increase farmers' access to improved and effective storage technologies.
