Rice-wheat cropping system (RWCS), a lifeline for the majority of the population in South Asia is under stress, due to the imbalanced and indiscriminate use of fertilizers. Therefore, we conducted an on-farm study at eight locations (Amritsar, Katni, Nainital, Samba, Pakur, Kanpur, Ambedkarnagar, and Dindori) covering five agro climatic zones of six Indian states (Jammu and Kashmir, Punjab, Uttarakhand, Uttar Pradesh, Madhya Pradesh, and Jharkhand) to (i) calculate the partial factor productivity (PFP) and agronomic use efficiency (AUE) to judge the response of NPK and Zn on grain yield of rice and wheat in RWCS and (ii) to work out the economic feasibility of different combinations of NPK in rice and wheat. Seven fertilizer treatments: Control (0-0-0), N alone (N-0-0), NP (N-P-0), NK (N-0-K), NPK (N-P-K), NPK+Zn (N-P-K-Zn), and FFMP (Farmers Fertilizer Management Practice) were assigned to all the locations. The levels of applied nutrients were used as per the standard recommendation of the location. The average of all the locations showed that the use of NP enhances the grain yield of rice and wheat by 105% and 97% over control, respectively. System productivity of RWCS was expressed in terms of rice grain equivalent yield (RGEY), Mg ha−1. Among the locations, Samba recorded the lowest productivity of RWCS with fertilizer treatments. In contrast, the highest productivity of RWCS with fertilizer treatments was recorded at Amritsar, except with NPK and NPK+Zn fertilization, where Katni superseded the Amritsar. An approximately 3-fold productivity gain in RWCS was recorded with the conjoint use of NP over control across the locations. Overall, the results of our study showed that the balance application of NPK increased the productivity of RWCS 245% over control. Partial factor productivity of Nitrogen (PFPn) N alone in rice varied across locations and ranged from 19 kg grain kg−1 N at Pakur to 41 kg grain kg−1 N at Amritsar. PFPn of N alone in wheat also ranged from 15.5 kg grain kg−1 of N at Ambedkarnagar to 28 kg grain kg−1 N at Amritsar. However, across locations the mean value of PFPn of N alone was 29 kg grain kg−1 N in rice and 21 kg grain kg−1 N in wheat. PFPn increased when combined application of N and P sorted in both rice and wheat across the locations. Similarly, combined application of NPK increased partial factor productivity of applied phosphorus (PFPp) in both the crops at all the locations. The combined application of NPK increased the PFPk for applied K at all the location. The response of K application with N and P when averaged over the location was 114% in rice and 93% in wheat over the combined use of N and K. In our study, irrespective of fertilizer treatments, the agronomic use efficiency of applied N (AUEn) and agronomic use efficiency of applied P (AUEp) were greater in rice than in wheat across the location. With regards to the economics, the mean net monetary returns among the fertilizers treatments was minimum (INR 29.5 × 103 ha−1) for the application of N alone and maximum (INR 8.65 × 103 ha−1) for application of NPK+Zn. The mean marginal returns across the locations was in order of N alone > NK > FFM > NPK > NP > NPK+Zn.

Improving the farming system to attain the household level self‐sufficiency, land utilization efficiency and sustainable livelihood security depends on better socio‐economic and ecological aspects of the systems practiced by the small farmers in the semiarid regions of India. The higher sustainable livelihood security index (SLSI) in the integrated crops‐livestock system helps to restore economic and ecological sustainability. The aim of this study was to analyze how various modules of farming in different combinations have interacted with the diversification of existing systems. We used the ecological security index (ESI), economic efficiency index (EEI), and social equity index (SEI) to produce a SLSI of improved integrated farming system (IFS) compared with benchmark farming in semiarid regions. In this study, different existing IFS comprising of seven modules (field crops, dairy, goats, poultry, horticulture, fishery, and apiary) in different combinations were examined. Results revealed that 72.5% farmers preferred to integrate two modules, where as 95% of farmers adopted field crops + dairy (FC + D) in preference to other modules. The sustainability indicators: ESI (+43.3%), EEI (+16.0%), SEI (+11.6%), and SLSI (+6.0%) were improved in the FC + D farming system to a greater degree than other IFS module combinations. Similarly, improved IFS interventions also increased sustainability indicators over benchmark farming. Based on large scale household studies over 5 years, our findings suggest that the improved IFS succeeded in providing a wider array of livelihood security than existing practices. Hence, the paradigm shift from component approach to an IFS‐based resilient system, which is economically viable, environmentally sustainable, and socially acceptable, is needed for development of semiarid regions.

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Adoption of an integrated farming system (IFS) is essential to achieve food and nutritional security in small and marginal holdings. Assessment of IFS to know the resource availability and socio-economic condition of the farm household, farm typology plays a critical role. In this regard, a sample survey of 200 marginal households practicing mixed crop-livestock agriculture was conducted during 2018–2019 at Southern Coastal Plains, which occupies 19,344 ha in Thiruvananthapuram district, Kerala, India. Farming system typology using multivariate statistical techniques of principal component analysis and cluster analysis characterized the diverse farm households coexisting within distinct homogenous farm types. Farming system typology identified four distinct farm types viz. resource constrained type-1 households with small land owned, high abundance of poultry, very low on-farm income, constituted 46.5%; resource endowed type-2 households oriented around fruit and vegetable, plantation crop, with a moderate abundance of large ruminant and poultry, high on-farm income, constituted 12.5%; resource endowed type-3 household oriented around food grain, extensive use of farm machinery, with a moderate abundance of large ruminant, low on-farm income, constituted 21.5%; and resource endowed type-4 household oriented around fodder, with high abundance of large ruminant, medium on-farm income, constituted 19.5% of sampled households. Constraint analysis using constraint severity index assessed the severity of constraints in food grain, horticulture, livestock, complementary and supplementary enterprises in each farm type, which allowed targeted farming systems interventions to be envisaged to overcome soil health problems, crops and animal production constraints. Farming system typology together with constraint analysis are therefore suggested as a practical framework capable of identifying type-specific farm households for targeted farming systems interventions.