Economic analysis of potential of citrus and walnut fruits by artificial neural network

South Asian countries have a wealth of opportunities to use the rainfed lands to the farmers’ advantage with the largest amount of rainfed land. The economic circumstances of the farmers operating in these areas are appalling due to the inefficient use of these lands. The work reported in this paper was carried out in the Jammu, Kathua, and Udhampur districts of the Jammu division. Two horticultural crops, viz., citrus and walnuts, were discovered to be cultivated in the chosen sample location. The influence of several elements to the financial potential of these horticultural crops was investigated using production functional analysis and marginal value productivity (MVP). The use of artificial neural networks (ANNs) further assisted this. According to a production functional analysis, the main variables in the districts of Udhampur and Kathua are machine labour and fertilisers, followed by human labour and fertilisers in the Jammu district. However, sensitivity analysis revealed the importance of manure, fertilisers, and manpower. In the rainfed portions of Jammu division, manpower combined with fertilisers is often thought of as the key determining factor for the profitability of horticulture crops like citrus and walnut. The absence of better varieties was identified via Garett ranking as the main restriction, followed by a lack of knowledge and expensive inputs, respectively. Article Highlights Human labour, machine labour and fertilizers proved to be the important factors for farmers’ income in the production function analysis. Human labour, manure and PPC turned out to be indispensable factors for farmers’ income in the ANN model. Non-availability of the improved varieties found out to be the major constraint for the farmers in the rainfed regions.