SUMMARYThe area under hybrid maize cultivation is increasing rapidly across South Asia. However, information regarding the proper nutrient management for modern stay-green maize hybrids in India is not adequate resulting in low productivity. Existing nutrient management practices are not able to capture the momentum change in the scenario of soil nutrient supply capacity and plant nutrient demand for achieving higher yield target. The present study aims at establishing the site-specific nutrient management (SSNM) package for an inceptisol (West Bengal, India). Soil indigenous nutrient supply capacity and nutrient use efficiency was also evaluated by using the nutrient omission plot technique. The experiment was laid out in strip-plot design, assigning three maize hybrids (P 3522, P 3396 and Rajkumar) in the vertical strip and nine fertilizer treatments [50% RDF/Recommended dose of fertilizer, 75% RDF, 100% RDF (200-60-60 kg N-P2O5-K2O ha−1), 125% RDF, 150% RDF, 100% PK, 100% NK, 100% NP and control (zero-NPK)] in the horizontal strip, with three replications. Results of the experiment revealed that the differences among cultivars were generally non-significant. The maize hybrids showed greater yield response to fertilization with N (4.14 Mg ha−1) during winter, followed by K (2.54 Mg ha−1) and P (1.58 Mg ha−1). Indigenous nutrient supply was estimated 107.2, 37.6 and 107.7 kg ha−1 for N, P and K, respectively. Both average agronomic efficiency (AE) and recovery efficiency (RE) were increased with 50% RDF and it decreased with further increase in NPK levels up to 150% RDF. The average internal efficiency (IE) was higher with 50% RDF closely followed by the treatment with absence of N. As grain yields and gross return over fertilizer (GRF) under 75 to 150% NPK treatments were similar, nutrient doses of 150 kg N, 45 kg P2O5 and 45 kg K2O ha−1 were recommended as optimum for maize hybrids.
The shock of Coronavirus Disease 2019 (COVID-19) has disrupted food systems worldwide. Such disruption, affecting multiple systems interfaces in smallholder agriculture, is unprecedented and needs to be understood from multi-stakeholder perspectives. The multiple loops of causality in the pathways of impact renders the system outcomes unpredictable. Understanding the nature of such unpredictable pathways is critical to identify present and future systems intervention strategies. Our study aims to explore the multiple pathways of present and future impact created by the pandemic and “Amphan” cyclonic storm on smallholder agricultural systems. Also, we anticipate the behaviour of the systems elements under different realistic scenarios of intervention. We explored the severity and multi-faceted impacts of the pandemic on vulnerable smallholder agricultural production systems through in-depth interactions with key players at the micro-level. It provided contextual information, and revealed critical insights to understand the cascading effect of the pandemic and the cyclone on farm households. We employed thematic analysis of in-depth interviews with multiple stakeholders in Sundarbans areas in eastern India, to identify the present and future systems outcomes caused by the pandemic, and later compounded by “Amphan”. The immediate adaptation strategies of the farmers were engaging family labors, exchanging labors with neighbouring farmers, borrowing money from relatives, accessing free food rations, replacing dead livestock, early harvesting, and reclamation of waterbodies. The thematic analysis identified several systems elements, such as harvesting, marketing, labor accessibility, among others, through which the impacts of the pandemic were expressed. Drawing on these outputs, we employed Mental Modeler , a Fuzzy-Logic Cognitive Mapping tool, to develop multi-stakeholder mental models for the smallholder agricultural systems of the region. Analysis of the mental models indicated the centrality of “Kharif” (monsoon) rice production, current farm income, and investment for the next crop cycle to determine the pathways and degree of the dual impact on farm households. Current household expenditure, livestock, and soil fertility were other central elements in the shared mental model. Scenario analysis with multiple stakeholders suggested enhanced market access and current household income, sustained investment in farming, rapid improvement in affected soil, irrigation water and livestock as the most effective strategies to enhance the resilience of farm families during and after the pandemic. This study may help in formulating short and long-term intervention strategies in the post-pandemic communities, and the methodological approach can be used elsewhere to understand perturbed socioecological systems to formulate anticipatory intervention strategies based on collective wisdom of stakeholders.
A field experiment was carried out during kharif season of 2010 and 2011 at Sriniketan Research Farm, Visva-Bharati, West Bengal, India. The yield attributes and growth parameters were significantly higher in case of sole maize and intercropping treatments with legumes. The maximum total chlorophyll (chlorophyll a + chlorophyll b) was observed on sole maize, which was statistically at par with maize crop under intercropping system. In the middle canopy, highest light interception (%) was observed in maize + groundnut (2:4). The grain yield (2.48 t ha-1) and stover yield (5.07 t ha-1) of maize were significantly higher in sole maize than either of its intercropping systems with legumes. The legume yield was highest in maize + groundnut (1:2) followed by sole groundnut. The maize equivalent yield (7.06 t ha-1) was highest in maize + groundnut (2:4) followed by maize + groundnut (1:2). The highest benefit cost ratio maize + groundnut (1:2) closely followed by maize + soybean (1:2). The total N uptake by sole maize was significantly higher and under intercropping systems, the highest N concentrations in grain and straw, and protein content in grains were obtained in maize + soybean (1:2) and maize + groundnut (2:4) treatment. DOI: http://dx.doi.org/10.3329/sja.v12i1.21118 SAARC J. Agri., 12(1): 117-126 (2014)
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, socioeconomic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socioeconomic , 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, socioeconomic 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.
In the present two-year study, an attempt was made to estimate the grain yield, grain nutrient uptake, and oil quality of three commonly grown maize ( Zea mays L.) hybrids fertilized with varied levels of nitrogen (N), phosphorus (P) and potassium (K). Results obtained from both the experimental years indicated that application of 125% of recommended dose of fertilizer (RDF) recorded maximum grain yield (10.37 t ha -1 ; 124% higher than control). When compared with 100% RDF, grain yield reduction with nutrient omission was 44% for N omission, 17% for P omission, and 27% for K omission. Nitrogen uptake was increased with increasing NPK levels up to 150% RDF that was statistically at par ( p ≥ 0.01) with 125% RDF. Increasing trend in P and K uptake was observed with successive increase in NPK levels up to 125% RDF, above which it declined. The protein content was significantly higher in grains of var. P 3396 with 125% RDF. Nutrient management has significant ( p ≤ 0.01) role in the grain oil content. Saturated fatty acids (palmitic, stearic and arachidic acid) content decreased, and unsaturated fatty acid (oleic, linoleic and linolenic acid) increased with increasing NPK levels. The average oleic acid desaturation and linoleic acid desaturation ratios were increased with increasing NPK levels up to 100 and 125% RDF, respectively. However, average monounsaturated fatty acids (MUFA): poly-unsaturated fatty acids (PUFA), saturated: unsaturated as well as linoleic: linolenic acid ratios were increased on receiving 75% RDF, and beyond that it showed decreasing trend. The omission of K had the highest inhibitory effect on corn oil quality followed by N and P omission.
It is critical to understand nutrient dynamics within different plant parts to correctly fine-tune agronomic advices, and to update breeding programs for increasing nutrient use efficiencies and yields. Farmer’s field-based research was conducted to assess the effects of nitrogen (N), phosphorus (P), and potassium (K) levels on dry matter and nutrient accumulation, partitioning, and remobilization dynamics in three popular maize ( Zea mays L.) hybrids (P3522, P3396, and Rajkumar) over two years in an alluvial soil of West Bengal, India. Experimental results revealed that NPK rates as well as different cultivars significantly ( p ≤ 0.05) influenced the dry matter accumulation (DMA) in different plant parts of maize at both silking and physiological maturity. The post-silking dry matter accumulation (PSDMA) and post-silking N, P, and K accumulations (PSNA, PSPA, PSKA) were highest in cultivar P3396. However, cultivar P3522 recorded the highest nutrient remobilizations and contributions to grain nutrient content. Total P and K accumulation were highest with 125% of the recommended dose of fertilizer (RDF) while total N accumulation increased even after 150% RDF (100% RDF is 200 kg N, 60 kg P 2 O 5 , and 60 kg K 2 O ha –1 for the study region). Application of 125% RDF was optimum for PSDMA. The PSNA continued to increase up to 150% RDF while 125% RDF was optimum for PSPA. Cultivar differences significantly affected both remobilization efficiency (RE) and contribution to grain nutrient content for all tested macronutrients (N, P, and K). In general, RE as well as contribution to grain nutrient content was highest at 125% RDF for N and K, and at 100% RDF for P (either significantly or at par with other rates) for plots receiving nutrients. For all tested cultivars, nutrient remobilization and contribution to grain nutrient content was highest under nutrient-omission plots and absolute control plots. Both year and cultivar effects were non-significant for both grain and stover yields of maize. Application of 75% RDF was sufficient to achieve the attainable yield at the study location. The cultivar P3522 showed higher yield over both P3396 and Rajkumar, irrespective of fertilizer doses, although, the differences were not statistically significant ( p ≥ 0.05). The study underscores the importance of maize adaptive responses in terms of nutrients accumulation and remobilization at different levels of nutrient availability for stabilizing yield.
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