gender and inorganic nitrogen what are the implications of moving towards a more balanced use of nitrogen fertilizer in the tropics

ABSTRACTFor agriculture to play a role in climate change mitigation strategies to reduce emissions from inorganic nitrogen (N) fertilizer through a more balanced and efficient use are necessary. Such strategies should align with the overarching principle of sustainable intensification and will need to consider the economic, environmental and social trade-offs of reduced fertilizer-related emissions. However, the gender equity dimensions of such strategies are rarely considered. The case studies cited in this paper, from India, Lake Victoria in East Africa and more broadly from sub-Saharan Africa (SSA), show that the negative externalities of imbalanced inorganic N use in high- and low-use scenarios impact more strongly on women and children. We examine, through a literature review of recent work in SSA, the relative jointness of intra-household bargaining processes in low N use scenarios to assess the degree to which they impact upon N use. We suggest that gender-equitable strategies for achieving more ba..

The optimization of conservation agriculture practices requires attention to location-specific performance: Evidence from large scale gridded simulations across South Asia

The ways in which farmers implement conservation agricultural (CA) practices – which entail reduced tillage, maintenance of soil cover, and crop rotations – varies considerably in different environments, farming systems, and by the intensity with which farmers administer management practices. Such variability requires an efficient tool to evaluate the cost-benefit of CA, to inform agricultural policymakers and development priorities to facilitate expanded use of CA under appropriate circumstances. Rice-wheat rotation is the principal production system in South Asia (SA). Research has shown that CA can be promising in this rotation because of improved irrigated water, energy, and labor use efficiencies, in addition to the reduction in atmospheric pollution and potentially long term improvements in soil quality. Yield responses to CA are however varying across studies and regions. With a nine-year rice-wheat CA experiment in Eastern Gangetic Plains of South Asia, this study parameterizes the Environmental Policy Climate (EPIC) model to simulate five CA and conventional managements on the RW cropping system. Information from geospatial datasets and farm surveys were used to parameterize the model at the regional scale, increasing the management flexibility and range of localities in the simulation. Yield potential of the CAs in the whole SA was thereby explored by utilizing the model with various management strategies. Our results demonstrate how geospatial and survey data, along with calibration by a long-term experiment, can supplement a regional simulation to increase the model's ability to capture yield patterns. Yield gains from CA are widespread but generally low under current management regimes, with varied yield responses among CAs and environments. Conversely, CA has considerable potential in SA to increase rice-wheat productivity by up to 38%. Our results highlight the importance of applying an adaptive definition of CA, depending on environmental circumstances, while also building the capacity of farmers interested in CA to apply optimal management practices appropriate for their environment

Cost-effective opportunities for climate change mitigation in Indian agriculture

This work was jointly carried out by International Maize and Wheat Improvement Center (CIMMYT) and University of Aberdeen and funded by the CGIAR research program on Climate Change, Agriculture and Food Security (CCAFS). CCAFS’ work is supported by CGIAR Fund Donors and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this paper cannot be taken to reflect the official opinions of these organizations. We sincerely acknowledge the input and support provided by various stakeholders in India during stakeholder meetings. We are thankful to Gokul Prasad for graphics assistance.Peer reviewedPublisher PD

Recent approaches in nitrogen management for sustainable agricultural production and eco-safety

Among plant nutrients, nitrogen (N) is the most important. Its importance as a growth- and yield-determining nutrient has led to large and rapid increases in N application rates, but often with poor use efficiency. Nitrogen management requires special attention in its use so that the large losses can be minimized and the efficiency maximized. Site-specific nutrient management (SSNM) has been found especially useful to achieve the goals of improved productivity and higher N use efficiency (NUE). Leaf color charts and chlorophyll meters assist in the prediction of crop N needs for rice and wheat, leading to greater N-fertilizer efficiency at various yield levels. Crop simulation models can be used in combination with field information and actual weather data to make recommendations to achieve higher NUE. Remote sensing tools are also used to predict crop N demands precisely. At the same time, traditional techniques like balanced fertilization, integrated N management (INM), use of nitrification inhibitors and slow-release nitrogenous fertilizers (SRNF), split application and nutrient budgeting, among others, are also used to supplement recent N management techniques to attain higher productivity and NUE, and reduce environmental pollution through the leakage of fertilizer N

Effect of fertility levels and bioinoculants on growth, productivity and economics of cluster bean (Cyamopsis tetragonoloba)

A field experiment was conducted at research farm of SKN College of Agriculture, Jobner, Rajasthan to assess the effect of fertility and bioinoculants on growth, yield and economics of cluster bean (Cyamopsis tetragonoloba L.). The results revealed that application of 75% recommended dose of fertilizer along with Rhizobium inoculation recorded higher growth (plant height, branches/ plant, dry matter accumulation/plant and nodules/plant); yield attributes (pods/plant, seeds/pod and 1000-seed weight) seed and stover yields, gross returns (25.05 × 103 `/ha), net returns (13.63 × 103 `/ha) and B:C ratio (1.19) as compared to control and phosphate solubilizing bacteria (PSB) inoculation and remained at par with all other treatment combination. So, it was concluded that use of 75% RDF along with Rhizobium may be recommended for obtaining the higher yield of Cluster bean in the region

Strategic selection of white maize inbred lines for tropical adaptation and their utilization in developing stable, medium to long duration maize hybrids

White maize plays an important role in human diet, especially in traditional crop growing regions of northern hill region, north-eastern states and central-western parts of India. Breeding efforts to enhance the genetic potential of white maize was not so prominent as compared to yellow maize in the country. As a result, genetic base of the material utilized in white maize breeding program in India is very narrow and majorly contains indigenous germplasm and few introductions. Hence, efforts were made to use 365 white maize inbred lines from CIMMYT, Mexico, for breeding program. These new inbred lines were grown at winter nursery center, Indian Institute of Maize Research, New Delhi for its tropical adaptation. After preliminary evaluation, a total 47 inbred lines were selected and evaluated in randomized complete block design with two replications at Regional Maize Research and Seed Production Centre, Begusarai, Bihar, during rabi 2014. Out of this top performing 12 inbred lines viz, CML 47, CML 95, CML 314, CML 319, CML 377, CML 488, CML 494, CML 504, CML 517, CML 522, CML 531 and CML 538 were selected and were crossed in diallel manner to obtain 66 medium to long duration experimental hybrids. Stability analysis using AMMI model was done to identify adaptive hybrids with high yielding potentiality. According to the ASVi value obtained, the hybrid G38 appeared to be stable followed by G50 and G44. On the other hand, the hybrid G25 appeared as location specific hybrid suitable for high input conditions

Applicability of calibrated diffuse reflectance spectroscopy models across spatial and temporal boundaries

Diffuse reflectance spectroscopy (DRS) is an emerging soil testing approach. Although several studies have validated the DRS approach, limited efforts are made to assess the applicability of calibrated DRS models on new samples collected at different locations and/or time. To test such spatio-temporal applicability of calibrated DRS models, we collected surface soil samples from 1,112 smallholder farms during 2018 (T2018) and 607 farms during 2021 (T2021) covering seven districts of the Bundelkhand region of central India. The T2018 samples covered 7 development blocks; the T2021 samples were also collected from these blocks but from different sampling locations. Additionally, a new sampling site (Jhansi-Bamour block) was added during 2021 to create an independent test dataset. Collected samples were analysed for 17 soil parameters (basic soil properties, macronutrients, and micronutrients) and spectral reflectance over the visible to near-infrared region. Corresponding soil test crop response (STCR) ratings were also estimated. The Cubist model was calibrated in the T2018 dataset and tested against the T2021 dataset using the coefficient of determination (R2), root-mean-squared error (RMSE), and percentage relative error deviation (PRED) at 30% error threshold as performance statistics. Model applicability was assessed at each block level (site-specific), by dividing the study site into their two geology-specific regions, and by treating the entire dataset as a regional-scale spectral library. Results showed that DRS models calibrated on a finer scale (site-specific) are less efficient in estimating soil parameters in broader scale (geology-specific and regional-scale) test T2021 samples although their STCR ratings may safely be estimated at local scales. When site-specific data were aggregated to broader scales and T2018 dataset was spiked with 20% samples from the T2021 dataset, model performance improved for critical soil parameters such as soil organic carbon (SOC) contents and several plant nutrients and their ratings; application of such large-scale models also improved the estimation accuracy when applied to site-specific datasets. Exchangeable Ca and Mg, clay and SOC contents were frequently well-estimated with R2 values ranging from 0.54 to 0.93. Fine sand was the next best estimated soil property with R2 values in the range of 0.40–0.75. The STCR ratings estimated in the DRS approach matched the wet chemistry-based STCR ratings to the tune of 43 to 100%. Overall, as many as 60% of all new samples could be estimated with more than 70% accuracy for 8 out of 17 parameters. With the DRS approach tested on both spatially- and temporally-independent test datasets and, specifically, with high estimation accuracy of STCR ratings, our results suggest that the DRS approach may safely be used as a viable alternative to conventional soil testing in smallholder farms

Doubling maize (Zea mays) production of India by 2025 – Challenges and opportunities

Maize (Zea mays L.) is a commodity of high economic significance in India. Its demand and production is increasing more rapidly as compared to other major commodities. It is estimated that by 2025, India would require 50 million metric tonnes (MMT) maize grain, of which 32 MMT would be required in the feed sector, 15 MMT in the industrial sector, 2 MMT as food, and 1 MMT for seed and miscellaneous purposes. Over this, there would be about 10 MMT of export potential also. Thus, in the next 10 years there is a necessity and opportunity for doubling India's maize production from the current level of approximately 25 MMT. Prevalence of yield limiting biotic and abiotic stresses, lower adoption of modern production technologies in certain regions, extension and policy gaps, etc. remain major challenges before the Indian maize sector. Therefore, strong technological and policy interventions would be required to achieve the goal of doubling maize production. By 2025, productivity level of 5-6 tonnes/ha need to be targeted, in order to double the production without significant increase in acreage. Technological interventions like cultivar development and diversification, incorporation of stress resilience in the germplasm, accelerating the breeding process through new tools, and adoption of modern cultivation and protection practices including conservation agriculture technologies would play a key role in increasing the productivity. At the same time, policy interventions like strengthening of post-harvest handling infrastructure, price stabilization mechanisms, and value chains, streamlining of extension system, augmenting hybrid seed delivery mechanisms, appropriate policy on genetically modified seeds etc. will be essentially required

Rice yield gaps and nitrogen-use efficiency in the Northwestern Indo-Gangetic Plains of India: Evidence based insights from heterogeneous farmers’ practices

A large database of individual farmer field data (n = 4,107) for rice production in the Northwestern Indo-Gangetic Plains of India was used to decompose rice yield gaps and to investigate the scope to reduce nitrogen (N) inputs without compromising yields. Stochastic frontier analysis was used to disentangle efficiency and resource yield gaps, whereas data on rice yield potential in the region were retrieved from the Global Yield Gap Atlas to estimate the technology yield gap. Rice yield gaps were small (ca. 2.7 t ha−1, or 20% of potential yield, Yp) and mostly attributed to the technology yield gap (ca. 1.8 t ha−1, or ca. 15% of Yp). Efficiency and resource yield gaps were negligible (less than 5% of Yp in most districts). Small yield gaps were associated with high input use, particularly irrigation water and N, for which small yield responses were observed. N partial factor productivity (PFP-N) was 45–50 kg grain kg−1 N for fields with efficient N management and approximately 20% lower for the fields with inefficient N management. Improving PFP-N appears to be best achieved through better matching of N rates to the variety types cultivated and by adjusting the amount of urea applied in the 3rd split in correspondance with the amount of diammonium-phosphate applied earlier in the season. Future studies should assess the potential to reduce irrigation water without compromising rice yield and to broaden the assessment presented here to other indicators and at the cropping systems level

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R2, root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R2 ranging between 367 and 470 kg ha−1, 276–345 kg ha−1, and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha−1 for nitrogen application rate to 372 kg ha−1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha−1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data