Identification and characterization of farming systems simplify huge diversity of farm types in complex agro-ecosystems, which is of critical importance for precise technological intervention and informed policy support. Multivariate statistical techniques like Principal Component Analysis (PCA) and Cluster Analysis (CA) may be used for a wide variety of situations associated with farm typology delineation. The present study conducted in coastal saline India demonstratively established the usefulness of such methodology in identification of predominant farm types and their characterization. Data collected from 144 farm households through questionnaire survey could identify four predominant farm types with differential income sources and resource-base. The methodological perspective employed in the study may be used as a decision support tool by extension agencies. On other hand, a differentiated, holistic and broad-based extension intervention with suitable institutional arrangement will be needed to address the need of these identified farm types. This will lead to a reduced transaction cost of the agricultural research and extension systems in diverse ecosystems in India and many similar situations in the developing countries.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Smallholder farms in developing countries are key to global food security and sustainability of agricultural systems. Suitable tools are needed to assess the utility of these systems for long-term policy interventions. We outline the development of a sustainability index for smallholder systems by amalgamating ecological frameworks and sustainable livelihood framework. We critically describe the process involved in the development of the indicator framework, selection of indicators, their measurement and assignment of weight, and aggregation to arrive at a composite index. A practical tool based on this index may prove helpful for long-term sustainability assessment in smallholder farms of developing countries.

The aim of this paper was to investigate the key factors limiting maize (Zea mays L.) productivity in eastern India to develop effective crop and nutrient management strategies to reduce yield gap. A series of farm surveys was conducted in two distinct agro-ecological zones of eastern India to evaluate the importance of crop management and structural constraints for maize productivity in a range of socio-economic settings prevalent in smallholder farms. Surveys revealed yield gap and yield variations among farms across growing seasons. Lower yields of farmers were mainly associated with farmer's ethnic origin, availability of family labor, land ownership, legumes in cropping sequence, irrigation constraints, seed type, optimal plant population, labor and capital investment, and use of organic manure. These constraints varied strongly between sites as well as growing seasons. Stochastic Frontier Analysis suggested intensification of farm input use and removal of socio-economic and structural constraints for increasing efficiency in maize production. The use of multivariate classification and regression tree analysis revealed that maize yield was affected by multiple and interacting production constraints, differentiating the surveyed farms in six distinct resource groups. These farm types lend scope for introducing typology-specific crop management practices through appropriate participatory on-farm evaluation/trials. Summarily, this research indicated that interacting production constraints should be addressed simultaneously, considering the need of different farm types, if significant productivity improvements are to be achieved. This will be, however, more challenging for less endowed farms due to lack of social and financial capital to improve management intensity.A typology-specific farm support strategy may be formulated to offset this lack of entitlement among resource-poor farmers