Optimal Crop Production Research Articles

Residual effect of legumes on maize yield, nitrogen balance and soil organic carbon stabilization under legume – maize cropping systems

Inclusion of legumes in cereal based cropping system restores soil fertility and improves carbon storage and soil quality. In this background a field experiment was conducted in a strip-split plot design using three legume species viz., green gram, groundnut and cluster bean as vertical strip, two residue management practices i.e. residue removal (-R) and residue retention (+R) as horizontal strip, and three nitrogen levels i.e. 100%, 75% and 50% recommended rate of nitrogen (N) application in intersectional sub plots with four replications during summer 2017 and 2018. The grain and stover yield of maize, N uptake and soil properties were monitored after each crop harvest; soil nitrogen balance and soil organic carbon were estimated. Under 100% N fertilization, cluster bean – maize cropping system with residue (+R) recorded 12.78 and 9.78% higher maize grain and stover yield, respectively than without residue (–R) treatment. Highest available N (189.9 kg ha−1) and available P (54.5 kg ha−1) were registered under cluster bean- maize cropping system. Legume residue incorporation resulted 9.65, 8.46, 7.09 and 8.86% higher (p = 0.05) WBC content, WBC stock, available N and P contents over residue removal scenario. Annually, 1.71% added carbon converted into soil organic carbon. Legume residue retention and 100% recommended rate of nitrogen application enhanced nitrogen balance. Soil N balance was highest under groundnut – maize system (165 kg ha−1). Overall, for better soil health and optimum crop production, cluster bean – maize cropping system with legume residue retention and 100% N fertilization may be promoted in semi-arid Western India.

Towards Sustainable Agriculture: Integrating Agronomic Practices, Environmental Physiology and Plant Nutrition

Sustainable agriculture aims to meet the growing global demand for food without compromising environmental integrity. This review paper explores the integration of agronomic practices, environmental physiology, and plant nutrition as pathways toward sustainable crop production. Intensive agricultural practices have led to significant problems such as soil degradation, loss of biodiversity, and environmental pollution. Recent advancements in plant physiology have enhanced our understanding of plant growth and response to biotic and abiotic stresses. Optimizing crop production in a sustainable manner depends on insights into key processes like photosynthesis, water use efficiency, and nutrient uptake. This review discusses the role of plant growth-promoting microorganisms (PGPMs) as biofertilizers, providing an alternative to harmful agrochemicals. PGPMs can improve soil health and promote plant growth when applied to soil, seeds, or plant surfaces, reducing reliance on synthetic fertilizers and pesticides. Additionally, nanotechnology in agriculture is examined, illustrating how nanomaterials (NMs) and nano-based fertilizers can enhance nutrient absorption and increase crop yields. Beyond these scientific approaches, there is an urgent need to implement novel agricultural practices that support sustainability. The world population is growing at a disquieting rate, placing tremendous pressure on natural resources. This threatens future generations' access to nutritious food and clean air. Innovative approaches like climate-smart agriculture, organic farming, biodynamic agriculture, sustainable intensification, and regenerative agriculture, along with practices such as integrated farming systems, precision agriculture, integrated nutrient management, and integrated pest management, have proven to safeguard agricultural sustainability. By integrating these innovative approaches and practices, it is possible to meet the increasing demands for food while preserving the environment.

Survey on Optimal Crop Prediction using Soil and Weather Analysis

This survey paper provides a comprehensive overview of methodologies, technologies, and approaches for analyzing soil environments to optimize crop production. It covers traditional soil sampling, advanced techniques like remote sensing, and real-time monitoring using sensor networks. The integration of sensor data with IoT and machine learning for decision support is explored. Soil health assessment and site-specific management strategies enabled by precision agriculture are emphasized. Challenges such as data integration and sensor accuracy are addressed, along with future research directions including low-cost sensing solutions and improved data analytics. Overall, the paper serves as a valuable resource for optimizing crop production sustainably, contributing to efficient agricultural practices in a changing climate

Impacts of Soil Health Practices on Hydrologic Processes

This paper explores the growing interest in soil health, emphasizing its importance in optimizing crop production, ecosystem function, and biodiversity. Defined by the USDA-NRCS as the soil’s capacity to function as a vital ecosystem, soil health involves filtering contaminants, cycling nutrients, supporting infrastructure, and regulating water movement. Traditional approaches to quantifying soil health focus on chemical, physical, or biological properties, often calling for a more integrated measurement method. While practices enhancing soil health, such as no-tillage, cover crops, and biodiversity, have long been promoted, their broader impacts on the hydrologic cycle are less documented. This paper aims to fill this gap by reviewing the literature on soil health practices’ effects on the hydrologic cycle and providing evidence and guidelines for policy- and decision-makers. It highlights the benefits of improved soil health, including increased water infiltration, higher crop yields, and reduced greenhouse gas emissions.

Harmonizing Agriculture: Nurturing Soil through Micronutrients Management and Texture Dynamics

The present review delves into the essential aspect of micronutrient management and soil texture dynamics in agriculture thus emphasizing their pivotal roles in sustaining soil health and optimizing crop productivity. Micronutrients, though required in trace amounts are critical for various physiological and biochemical processes in plants. Micronutrient deficiencies can profoundly impact plant growth and yield, necessitating effective management strategies such as soil amendments, foliar applications, and balanced fertilization practices. Additionally, the paper explores the potential of soil microbes to enhance micronutrient availability. Soil testing emerges as a crucial tool for monitoring nutrient levels and guiding nutrient application decisions. Furthermore, soil texture dynamics significantly influence nutrient availability, highlighting the importance of soil composition and structure. Conservation tillage practices, crop rotation, and the use of micronutrient fertilizers are discussed as means to promote soil health and optimize nutrient profiles. Overall, the paper underscores the importance of integrating micronutrient management and texture dynamics for harmonizing agriculture, ensuring sustainable soil health, and fostering increased crop yields.

Herbicide Strategies for Weed Control in Rice Cultivation: Current Practices and Future Directions

Rice, as a staple food for more than half of the world's population, plays a crucial role in global food security. However, weed competition poses a significant challenge to rice cultivation, affecting yield and quality. To address this challenge, rice farmers have increasingly turned to chemical herbicides as a cost-effective alternative to labor-intensive manual weeding. This review paper explores the current practices and future directions in herbicide strategies for weed control in rice cultivation, with a specific focus on diverse approaches adopted in various agro-ecological regions, particularly in Pakistan. The paper begins by highlighting the importance of rice cultivation in global agriculture and the economic significance of the crop, emphasizing the need for effective weed management to ensure sustainable production. It discusses the impact of labor shortages and rising labor costs on weed control practices, driving the adoption of chemical herbicides, especially in direct-seeded rice cultivation. Additionally, the review underscores the importance of integrated weed management systems, which combine cultural practices, targeted herbicide applications, and legislative measures to optimize crop productivity while minimizing environmental impact. Furthermore, the paper evaluates the efficacy of different herbicides and their timing of application in managing weeds and maximizing rice yield. It synthesizes findings from recent studies to provide insights into the effectiveness of pre-emergence and post-emergence herbicides, as well as their impact on weed-crop competition and overall crop health. By analyzing the strengths and limitations of existing herbicide strategies, the review identifies opportunities for innovation and improvement in weed management practices. Overall, this review paper offers a comprehensive overview of herbicide strategies for weed control in rice cultivation, highlighting the need for sustainable and integrated approaches to address weed challenges while ensuring food security and environmental sustainability in rice-producing regions.

Optimizing Crop Yield Prediction: Data-Driven Analysis and Machine Learning Modeling Using USDA Datasets

This research uses a variety of machine learning models and exploratory data analysis (EDA) to forecast crop yields using USDA information from 2003 to 2013 in an effort to achieve precision agriculture. Not only did we want to predict agricultural output, but we also wanted to identify the underlying factors that affect yield. By means of thorough EDA, which encompassed a wide range of agricultural data, including weather patterns and USDA-sourced soil composition, we were able to gain important insights into the variables that impact differences in crop output. The thorough investigation that followed served as the basis for our machine learning modelling. We thoroughly assessed and contrasted the performance of a variety of machine learning algorithms, including Bagging Regressor, KNN, Decision Trees, Gradient Boost, Random Forest, and Linear Regression. The accuracy of the models varied noticeably, as the results showed: the Random Forest, Decision Trees, and Bagging Regressor models showed great accuracy, with respective values of 98.56%, 97.62%, and 98.59%. Conversely, KNN and Linear Regression showed reduced accuracy, indicating their limits in this situation. The robustness of our results was further improved by applying k-fold cross-validation, highlighting the significance of model validation in crop yield prediction. Some models showed changes in accuracy during cross-validation, which revealed more about their dependability. In addition to providing a thorough investigation of the variables affecting agricultural productivity, this study highlights the diverse forecasting powers of machine learning models. Our findings provide a path for well-informed agricultural decision-making by utilizing technology to optimize crop production estimates. The ultimate goal of this research is to support stakeholders in optimizing agricultural productivity and enable sustainable practices.

Biomorphological Characteristics of Beetroot Depending on Sowing Schemes under Absheron Conditions

The issues of sowing beetroot according to various schemes are considered in order to determine the optimum crop production in the conditions of Absheron. The biomorphological features of beetroot have been studied. In terms of root productivity, the sowing schemes 55+55+70×10 and 45+45+70×10 was superior to other options for sowing beetroot plants. It was revealed that as the density of plants in a row increases, the number of leaves in them decreases, which is quite natural, since the plant tries to create an optimal leaf surface area.

Penerapan Analisis Biomassa Shoot-Root Ratio dalam Memprediksi Hasil pada Genotipe Ercis

The economic and nutritional potential of peas makes them highly promising for development. Accurate yield predictions based on photosynthate allocation are essential for optimizing crop production. This study aimed to utilize shoot-root ratio biomass analysis to predict the yield of different pea genotypes. The research was conducted at the experimental field of the Faculty of Agriculture, University of Brawijaya, located in Jatimulyo, Malang, East Java. The study utilized three pea genotypes (TMG-8-2, BTG-1, and BW-44181-3-1) planted in individual rows. The analysis involved calculating the shoot-root ratio and using simple linear regression to predict the results. The findings indicated that the shoot-root ratio varied over time, with the BW-44181-3-1 genotype displaying the highest ratio and the TMG-8-2 genotype showing the lowest. The shoot-root ratio exhibited the highest increase during the generative phase, peaking during pod maturation. Regarding yield variables, the results of the simple linear regression analysis revealed that the shoot-root ratio significantly influenced the number of pods planted (R2 = 88%), the number of seeds planted (R2 = 78%), and the weight of seeds planted (R2 = 90%) at 70 days after planting (DAP). An increase in the shoot-root ratio served as an indicator of the quantity and weight of seeds planted, as well as the number of pods produced.

Optimizing Potassium and Iron Levels for Enhanced Sunflower Yield in Peshawar Valley

Sunflower cultivation is integral to meeting the demand for edible oil seeds, necessitating the optimization of plant nutrient application for optimal crop production. A field trial conducted at the Agronomy Research Farm, The University of Agriculture, Peshawar, during the summer of 2022 aimed to assess the impact of varying potassium and iron levels on sunflower yield and yield components. Four levels of potassium (0, 30, 45, and 60 kg ha-1) and four levels of iron (0, 5, 10, 15 kg ha-1) were investigated using a randomized complete block (RCB) design with three replications, maintaining one control plot in each replication. Iron sulfate and Muriate of potash were utilized as sources of iron and potassium, respectively. Results revealed that applying potassium at a rate of 60 kg ha-1 led to maximum head diameter (18.38 cm), thousand-achene weight (47.56 g), biological yield (7476 kg ha-1), and grain yield (2039 kg ha-1) accompanied by taller plants (169.3 cm). Similarly, iron application at a rate of 15 kg ha-1 resulted in increased plant height (159.2 cm), maximum head diameter (18.01 cm), 1000 achene weight (47.8 g), biological yield (7416 kg ha-1), and grain yield (2062 kg ha-1). Notably, the effect of iron on plant height was non-significant. Based on the findings, the application of potassium at 60 kg ha-1 and iron at 15 kg ha-1 is recommended for achieving higher sunflower yields in agro-climatic conditions of Peshawar valley. This study revealed the significance of tailored nutrient management strategies for maximizing sunflower productivity in specific region

Unraveling the molecular mechanisms governing axillary meristem initiation in plants.

Axillary meristems (AMs) located in the leaf axils determine the number of shoots or tillers eventually formed, thus contributing significantly to the plant architecture and crop yields. The study of AM initiation is unavoidable and beneficial for crop productivity. Shoot branching is an undoubted determinant of plant architecture and thus greatly impacts crop yield due to the panicle-bearing traits of tillers. The emergence of the AM is essential for the incipient bud formation, and then the bud is dormant or outgrowth immediately to form a branch or tiller. While numerous reviews have focused on plant branching and tillering development networks, fewer specifically address AM initiation and its regulatory mechanisms. This review synthesizes the significant advancements in the genetic and hormonal factors governing AM initiation, with a primary focus on studies conducted in Arabidopsis (Arabidopsis thaliana L.) and rice (Oryza sativa L.). In particular, by elaborating on critical genes like LATERAL SUPPRESSOR (LAS), which specifically regulates AM initiation and the networks in which they are involved, we attempt to unify the cascades through which they are positioned. We concentrate on clarifying the precise mutual regulation between shoot apical meristem (SAM) and AM-related factors. Additionally, we examine challenges in elucidating AM formation mechanisms alongside opportunities provided by emerging omics approaches to identify AM-specific genes. By expanding our comprehension of the genetic and hormonal regulation of AM development, we can develop strategies to optimize crop production and address global food challenges effectively.

Effect of lime, blended fertilizer and vermicompost on maize (Zea mays) yield in Assosa district, north-western Ethiopia

Application of organic and mineral fertilizers improves crop productivity and availability of plant nutrients. Further investigation into their integral application is crucial to obtaining optimum crop productivity. A field experiment was conducted during 2020–21 and 2021–22 at experimental field station in Benshangul Gumuz, Assosa University, Assosa, Ethiopia to investigate the response of maize (Zea mays L.) yield to lime, blended NPSZnB fertilizer and vermicompost. Two lime levels, three vermicompost levels, and three blended fertilizer levels were factorially combined and laid out in a randomized complete block design (RCBD). The integral application of organic fertilizer with blended fertilizer with lime significantly affected the agronomic parameters. Grain yield, number of grains per cob, 1000-seed weights, plant height, and days to 50% tasseling were parameters most affected by the amendments. Integral application of 50% of the recommended rate of blended fertilizer and vermicompost with lime improved maize yield by 53%, while the application of NPSZnB and vermicompost individually improved maize yield by 34% and 22% over the control treatment. One tonne of vermicompost and lime could substitute 38 kg and 23 kg of blended fertilizer, respectively. In conclusion, the integrated application of NPSZnB and vermicompost with the recommended amount of lime was the best soil fertility management option. In terms of grain yield and net benefit, 4 t/ha lime, 2.5 t/ha vermicompost and 100 kg/ha blended fertilizers were recommended for optimum maize production in the Assosa area and similar agroecologies.

Advanced Intelligent Systems for Quantitative Analysis of Crop Production and Efficiency in Maharashtra

Agriculture plays a pivotal role in the economic growth of any country, and India is no exception. Approximately 70% of India’s population depends on agriculture, either directly or indirectly. According to the Ministry of Agriculture and Farmers Welfare, the agricultural and allied sectors contribute 20.2% to the total economy’s Gross Added Value (GAV), per the Provisional Estimates of Annual National Income. Given India’s dense population, ensuring optimal crop production is essential to meet the nation’s food demand. This study focuses on the correlation between crop production and temperature in Maharashtra, a key agricultural state. Maharashtra produces a variety of crops, including staple cereals like jowar, rice, and wheat, as well as pulses such as gram, tur, and urad. It is also a significant producer of oilseeds, including groundnut, sunflower, and soybean. Additionally, important cash crops like sugarcane, cotton, and turmeric contribute to the state’s agricultural economy. As climate factors like temperature play a critical role in crop growth, this study aims to analyze how temperature fluctuations affect the yield and efficiency of various crops in Maharashtra to optimize agricultural productivity to support economic growth and food security.

Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction

Efficient and rational monitoring of plant health is an essential prerequisite for ensuring optimal crop production and resource management in the field of agriculture. Computer vision techniques have revolutionized visual disease monitoring with their exceptional visual recognition performance. However, despite the outstanding results, the widespread acceptance of these methods in agriculture practice is still in its early stages. This study presents a comprehensive survey of the next generation of computer vision models applied to plant disease monitoring in precision agriculture. Our study begins by tracing the evolution of agricultural computer vision research over the past decade, encompassing legacy methods such as convolutional neural networks (CNNs), progressing to newer techniques like vision transformers (ViTs), and culminating in cutting-edge vision multi-layer perceptrons (MLPs). Next, our study embraces both qualitative and quantitative approaches, supporting a profound review of literature and classifying methodologies and experimental approaches. A significant contribution lies in our comprehensive taxonomy, offering a fine-grained categorization of current computer vision models. This taxonomy meticulously highlights the potentials and limitations of these models while explaining their roles in improving plant disease management. Moreover, extensive experimental comparisons are conducted on PlantVillage dataset to evaluate the performance of state-of-the-art computer-vision models for plant recognition data. The obtained results are then utilized to draw insightful conclusions about the behavior of these models and provide guidance for selecting the most suitable one for specific tasks at hand. Additionally, we discuss open research avenues and future directions of computer-vision models in plant disease management including challenges related to the data scarcity, the computational efficiency, need for explainability, and multi-modal analysis.

Remote Sensing and Geographic Information Systems for Precision Agriculture: A Review

Precision agriculture aims to optimize crop production and minimise environmental impacts by using information technology, remote sensing, satellite positioning systems, and proximal data gathering. This review paper examines current applications and future directions of remote sensing and geographic information systems (GIS) for precision agriculture. Remote sensing provides data on crop health, soil conditions, water status, and yield which can guide variable rate applications within fields. Satellite and aerial platforms allow multispectral and hyperspectral imaging for vegetation indices analysis, crop classification, and stress detection. GIS technology integrates these data layers to model and map variations, develop prescription maps, and analyse spatial relationships. Key research frontiers include high-resolution satellite and drone data for within-field analysis, better integration of proximal and remote sensing, online nutrient and yield monitors, real-time prescription modelling, and predictive analytics using machine learning. Adoption continues to increase with better data analytics tools and greater economic returns realized. Remote sensing and GIS provide an integral platform for variable rate technologies, predictive modelling, and data-driven decision-making for precision agriculture.

Evaluating the use of ICT Tools in Precision Agriculture for Efficient Pesticide Management

The research study aims to investigate and evaluate the use of ICT (Information and Communication Technology) tools in precision agriculture for efficient pesticide management. Precision agriculture, as a modern approach to farming, leverages technology to optimize crop production practices. However, the potential benefits of ICT tools, specifically in pesticide management, require further investigation and assessment. The findings of this research endeavor will provide valuable insights into the role of ICT tools in precision agriculture for pesticide management. The evaluation will shed light on the benefits and challenges associated with deploying these tools, including their impact on reducing pesticide use, optimizing resource allocation, and promoting environmental sustainability. Moreover, the research will contribute to the identification of best practices and key considerations for the successful integration of ICT tools in the realm of precision agriculture. Overall, this research will enhance our understanding of how ICT tools can revolutionize pesticide management and pave the way for more efficient and sustainable agricultural practices. The findings will serve as a crucial reference for farmers, policymakers, and researchers seeking to optimize pesticide usage, minimize environmental risks, and improve overall crop yield in the context of precision agriculture.

Nanomaterial-Induced Modifications in Plant Physiology and Genetics for Optimal Crop Production Strategies

Nanotechnology has ushered in a new era in agriculture, offering transformative solutions to address the pressing challenges of feeding a burgeoning global population while adapting to a changing climate. At the forefront of this revolution are nanomaterials, characterized by their unique properties at the nanoscale. This article explores the intricate and dynamic relationship between nanomaterials and plants, unveiling how they induce profound changes in plant physiology and genetics. These changes, while complex, hold the key to unlocking novel approaches for crop improvement and sustainable agriculture. However, the core of this study delves into understanding how nanomaterials are taken up by plants and transported within their intricate biological systems. The mechanisms underlying nanomaterial uptake and distribution within plants are unveiled, offering possibilities for precise nutrient targeting and enhanced uptake efficiency. Subsequent sections meticulously dissect the consequences of nanomaterial exposure on plant physiology, including growth, development, and stress responses. The intricate genetic modifications and epigenetic changes that nanomaterials induce in plants are explored, revealing the potential for tailored crop improvement strategies. Notably, we demonstrate the practical implications of these nanomaterial-induced changes, showcasing their relevance for optimizing crop yields, resilience to environmental stressors, and nutritional quality. This article also takes a holistic approach by addressing the environmental and safety considerations that accompany the use of nanomaterials in agriculture. It emphasizes the necessity of responsible application, ecological impact assessment, and the establishment of regulatory frameworks to guide safe utilization. In conclusion, this article serves as an illuminating exploration of the nascent field where nanomaterials meet plant physiology and genetics, with implications that could reshape the future of agriculture.

DAWN: Dashboard for Agricultural Water Use and Nutrient Management—A Predictive Decision Support System to Improve Crop Production in a Changing Climate

Abstract Climate change presents huge challenges to the already-complex decisions faced by U.S. agricultural producers, as seasonal weather patterns increasingly deviate from historical tendencies. Under USDA funding, a transdisciplinary team of researchers, extension experts, educators, and stakeholders is developing a climate decision support Dashboard for Agricultural Water use and Nutrient management (DAWN) to provide Corn Belt farmers with better predictive information. DAWN’s goal is to provide credible, usable information to support decisions by creating infrastructure to make subseasonal-to-seasonal forecasts accessible. DAWN uses an integrated approach to 1) engage stakeholders to coproduce a decision support and information delivery system; 2) build a coupled modeling system to represent and transfer holistic systems knowledge into effective tools; 3) produce reliable forecasts to help stakeholders optimize crop productivity and environmental quality; and 4) integrate research and extension into experiential, transdisciplinary education. This article presents DAWN’s framework for integrating climate–agriculture research, extension, and education to bridge science and service. We also present key challenges to the creation and delivery of decision support, specifically in infrastructure development, coproduction and trust building with stakeholders, product design, effective communication, and moving tools toward use.

Enhancing Sustainable Crop Production through Innovations in Precision Agriculture Technologies

Precision agriculture technologies provide innovative tools to optimize crop production while minimizing environmental impacts. This review examines recent advances in precision ag systems to enhance sustainable agriculture. Key innovations include: remote and proximal crop sensing techniques leveraging hyperspectral imaging, thermal imaging, and Lidar to assess crop health and stress status; variable rate technologies like targeted sprayers and precision planters to reduce input waste; data analytics and decision support systems that integrate multi-source data streams to guide site-specific intervention; robotics and automation for precision field operations; and advanced breeding techniques and genomic tools enabling development of stress resilient, high yielding varieties. Adoption barriers, future technology trajectories, and priority research needs are discussed to further advance precision solutions that support productivity, efficiency, and sustainability goals.

Sustainable Water Management of Drip-Irrigated Asparagus under Conditions of Central Poland: Evapotranspiration, Water Needs and Rainfall Deficits

Water is a valuable yet scarce resource in agriculture. Optimizing crop production relies on irrigation, but within the framework of sustainable agriculture, efforts should prioritize reducing irrigation water usage. Unfortunately, climate change increases plant water requirements, consequently heightening the need for irrigation. The aim of the research was to estimate the water needs of asparagus during the fern growth period (21 June–31 August) using the Blaney–Criddle method. The study also aimed to determine trends in changing water needs and estimate precipitation deficits. Calculations were carried out for the years 1981–2020 across four provinces in central Poland. The research indicated that water needs varied between 233 mm and 242 mm, depending on the province. Over the forty-year period, all provinces displayed an increasing trend in water needs, with a rise of 3.1 mm to 6.2 mm per decade. Between 21 June and 31 August, rainfall deficits occurred with varying intensity in all provinces during normal, medium dry and very dry years. The values of water needs and rainfall deficit, calculated as a result of the research, are of key importance in the design, construction, and management of drip irrigation systems for asparagus plantations in central Poland.