Pearl millet-based cropping systems with intensive tillage operations prior to sowing have limited sustainable productivity in the low-irrigation conditions of semi-arid farming ecologies, such as those in the north Indian plains. The adoption of improved management practices such as zero tillage with residue retention (ZTR) and diversification with the inclusion of summer pulse crops has the potential to improve cropping system sustainability. Therefore, an experiment was designed to compare two improved management practices, zero tillage (ZT) and ZTR, to conventional tillage (CT), across three pearl millet-based cropping systems: pearl millet–chickpea (PM–CP), PM–CP–mungbean (MB), and PM–CP–forage pearl millet in a two-year experiment. Experimental treatments were compared in terms of pearl millet productivity, mineral biofortification, and greenhouse gas emissions. Results showed a significant increase in pearl millet yield attributes, grain and stover productivity, nutrient uptake, and micronutrient biofortification in the PM–CP–MB cropping system under ZTR relative to other treatment combinations. On-farm evaluation at different locations also showed that the intensification of PM–CP system using summer crops enhanced pearl millet productivity across diverse tillage systems. Overall, zero tillage practices combined with diversified pearl millet-based cropping systems are likely to be management practices, which farmers can use to sustainably maintain or increase cropping system productivity in the various semi-arid areas of the world.

Pearlmillet-chickpea cropping system (PCCS) is emerging as an important sequence in semi-arid regions of south-Asia owing to less water-requirement. However, chickpea (dry-season crop) faces comparatively acute soil moisture-deficit over pearlmillet (wet-season crop), limiting overall sustainability of PCCS. Hence, moisture-management (specifically in chickpea) and system intensification is highly essential for sustaining the PCCS in holistic manner. Since, conservation agriculture (CA) has emerged is an important climate-smart strategy to combat moisture-stress alongwith other production-vulnerabilities. Hence, current study comprised of three tillage systems in main-plots viz., Complete-CA with residue retention (CA_c), Partial-CA without residue-retention (CA_p), and Conventional-tillage (ConvTill) under three cropping systems in sub-plots viz., conventionally grown pearlmillet-chickpea cropping system (PCCS) alongwith two intensified systems i.e. pearlmillet-chickpea-fodder pearlmillet cropping system (PCFCS) and pearlmillet-chickpea-mungbean cropping system (PCMCS) in split-plot design. The investigation outcomes mainly focused on chickpea (dry-season crop) revealed that, on an average, there was a significant increase in chickpea grain yield under CA_cto the tune of 27, 23.5 and 28.5% under PCCS, PCFCS and PCMCS, respectively over ConvTill. NPK uptake and micronutrient (Fe and Zn) biofortification in chickpea grains were again significantly higher under triple zero-tilled CA_cplots with residue-retention; which was followed by triple zero-tilled CA_pplots without residue-retention and the ConvTill plots. Likewise, CA_cunder PCMCS led to an increase in relative leaf water (RLW) content in chickpea by ~ 20.8% over ConvTill under PCCS, hence, ameliorating the moisture-stress effects. Interestingly, CA-management and system-intensification significantly enhanced the plant biochemical properties in chickpea viz.,super-oxide dismutase, ascorbate peroxidase, catalase and glutathione reductase; thus, indicating their prime role in inducing moisture-stress tolerance ability in moisture-starved chickpea. Triple zero-tilled CA_cplots also reduced the N₂O fluxes in chickpea but with slightly higher CO₂emissions, however, curtailed the net GHG-emissions. Triple zero-tilled cropping systems (PCFCS and PCMCS) both under CA_cand Ca_pled to a significant improvement in soil microbial population and soil enzymes activities (alkaline phosphatase, fluorescein diacetate, dehydrogenase). Overall, the PCCS system-intensification with mungbean (PCMCS) alongwith triple zero-tillage with residue-retention (CA_c) may amply enhance the productivity, micronutrient biofortification and moisture-stress tolerance ability in chickpea besides propelling the ecological benefits under semi-arid agro-ecologies. However, the farmers should preserve a balance while adopting CA_cor CA_pwhere livestock equally competes for quality fodder.

Time series analysis and prediction have attained significant attention from the research community in the past few decades. However, the prediction accuracy of the models highly depends on the models’ learning process. In order to optimize resource usage, a better learning methodology, in terms of accuracy and learning time, is needed. In this context, the current research work proposes EvoLearn, a novel method to improve and optimize the learning process of neural-based models. The presented technique integrates the genetic algorithm with back-propagation to train model weights during the learning process. The fundamental idea behind the proposed work is to select the best components from multiple models during the training process to obtain an adequate model. To demonstrate the applicability of EvoLearn, the method is tested on the state-of-the-art neural models (namely MLP, DNN, CNN, RNN, and GRU), and performances are compared. Furthermore, the presented study aims to forecast two types of time series, i.e. air pollution and energy consumption time series, using the developed framework. In addition, the considered neural models are tested on two datasets of each time series type. From the performance comparison and evaluation of EvoLearn using a one-tailed paired T-test against the conventional back-propagation-based learning approach, it was found that the proposed method significantly improves the prediction accuracy.

Introduction: With increasing demand for food and changing environmental conditions, a better understanding of the factors impacting wheat yield is essential for ensuring food security and sustainable agriculture. By analyzing the effect of multiple factors on wheat yield, the presented research provides novel insights into the potential impacts of climate change on wheat production in India. In the present study, datasets consisting of countrywide environmental and agronomic factors and wheat yield were collected. In addition, the study also analyzes the effect of information demand of farmers on production.Methodology: The study employs a regional analysis approach by dividing the country into five zonal clusters: Northern Hills, Central India, Indo-Gangetic Plains, North-Eastern India, and Peninsular India. Correlation and Principal Component Analysis (PCA) were performed to uncover the month-wise key factors affecting wheat yield in each zone. Furthermore, four Machine Learning/Deep Learning-based models, including XGBoost, Multi-layer Perceptron (MLP), Gated Recurrent Unit (GRU), and 1-D Convolutional Neural Network (CNN), were developed to estimate wheat yield. This study estimated partial derivatives for all factors using Newton's Quotient Technique, a numerical method-based approach.Results: The analysis focused on applying this technique to the best-performing wheat yield estimation model, which was the GRU-based model (with RMSE and MAE of 0.60 t/ha and 0.46 t/ha, respectively).Discussion: In the later sections of the article, multiple policy recommendations are communicated based on the extracted insights. The results of the presented research help inform decision-making regarding the development of strategies and policies to mitigate the impacts of climate change on wheat production in India.