Soybean intensification-based farm typology for the Chinyanja Triangle

Agriculture remains a cornerstone of the economies in Malawi, Mozambique, and Zambia, with maize and soybean being two of the most significant crops cultivated in these regions. Maize dominates the cropping systems and serves as a staple food in the region, while soybean is identified as a strategic crop for transformation of the food systems (Giller et al., 2011). However, yields and agrarian livelihood outcomes for both crops, and many others, are depressed due to several challenges, including poor soil quality, limited productive resources, increasing occurrence of climate extremes, poor access to financial and information services, market volatility, and the dominance of middlemen who reduce farmers’ potential profits (Omondi et al., 2023; João Vasco Silva et al., 2023). Smallholder farmers, who make up 75% of the population, are primarily oriented towards subsistence agriculture. African governments recognize the importance of smallholder farmers, as addressing their challenges means addressing the challenges of a large proportion of their population. Additionally, excess production from smallholder farms can significantly contribute to national economies. Therefore, it is in the governments’ interests to re-orient farmers from subsistence to commercial agriculture, contributing to food imports reduction and production of raw materials for agro-industries (Li & Wang, 2016). There is great heterogeneity in smallholder agriculture in southern Africa, evident at different scales – from individual farms to local communities and across regions (Chikowo et al., 2014; João Vasco Silva et al., 2023; Zingore et al., 2011). There occurs a region transcending borders of Malawi, Mozambique and Zambia, where communities are united by cultural and linguistic ties, the Chichewa people in the Chinyanja triangle (Amede et al., 2017; Omondi et al., 2023). Within this region, is considerable spatial variability in biophysical and socioeconomic dimensions. A systematic approach to understanding this diversity and how different types interact with local institutions and policies lays a foundation for evidence-based interventions and policy advice. At the same time, there also occurs distinct differences in these regions, considering national policies, as they belong to different countries. It will be important to explore how these differences can affect each country’s trajectory. There are many ways to define typologies, including machine learning algorithms, participatory approaches and expert based models (Alvarez et al., 2018; Nyambo et al., 2019). Classification is carried out for a variety of reasons, including explaining variability in performance, targeting innovations, and providing a basis for policy formulation. Once critical variables are identified that can allocate farmers to a farm type within a typology, additional data can be added to the database to identify the farm type a specific entry belongs to. This enables additional farms to be allocated o a farm type at a lower data cost. Farm typologies can be used to generate recommendations for input use, intensity of production (J. V. Silva et al., 2023), and even the platform used for extension advisory delivery. For example, a typology based on digital capacity of farmers would identify that some farmers are not able to access digital extension approaches, with this specific aspect varying per farm type. For a typology based on soybean production intensification, developed to guide intensification, not every farmer can benefit in the same way from the soybean intensification, based on their capacity and production goals. Appropriate support from national governments can improve the spatial distribution and promotion of soybean production and appropriate agro-industries in specific districts (Giller et al., 2011). This comprehensive analysis will help to (i) uncover the heterogeneity among farmers in the Chinyanja Triangle in Malawi, Mozambique and Zambia, and highlight the unique characteristics that define each segment. By identifying distinct farmer segments, we can better (ii) tailor agricultural policies, extension services, and market strategies to meet the specific needs of each group. This segmentation will also provide insights into the challenges and opportunities faced by different types of farmers, thereby informing more effective and inclusive agricultural development programs. Ultimately, the goal is to contribute to the (iv) formulation of more nuanced and impactful agricultural policies that can drive sustainable growth and improve the livelihoods of maize and soybean farmers in these three countries, particular use of soybean production led commercialization. The objective of this work is to: (i) Classify farmers into different categories according to aggregated biophysical and socioeconomic measures, representing functional and structural variables.