Crop Management Research Articles

A Study on Enhancing Crop Yield Prediction with Accuracy through Machine and Deep Learning Models

Accurate crop yield prediction is essential for optimizing agricultural resource allocation and supporting farmers in making informed crop management decisions. This study investigates the effectiveness of various machine learning and deep learning models for predicting crop yields based on key agricultural parameters, including Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. We apply multiple algorithms—Logistic Regression, Multilayer Perceptron, Sequential Minimal Optimization (SMO), J48, Random Forest, REP Tree, and a proposed deep learning model—to evaluate their predictive accuracy in classifying agricultural items. Each model’s performance is assessed using metrics such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and the time taken to build the model. Findings reveal that while traditional machine learning models like Random Forest and REP Tree show robust performance, the proposed deep learning model demonstrates enhanced accuracy and efficiency, making it highly promising for crop yield prediction applications. This study underscores the potential of advanced learning algorithms to improve agricultural productivity by providing actionable insights for crop planning and resource management.

Adaptive Agronomic Strategies for Enhancing Cereal Yield Resilience Under Changing Climate in Poland

Climate-driven changes have raised concerns about their long-term impacts on the yield resilience of cereal crops. This issue is critical in Poland as it affects major cereal crops like winter triticale, spring wheat, winter wheat, spring barley, and winter barley. This study investigates how soil nutrient profiles, fertilization practices, and crop management conditions influence the yield resilience of key cereal crops over a thirteen-year period (2009–2022) in the context of changing climate expressed as varying Climatic Water Balance. Data from 47 locations provided by the Research Centre for Cultivar Testing were analyzed to assess the combined effects of agronomic practices and climate-related water availability on crop performance. Yield outcomes under moderate and enhanced management practices were contrasted using Classification and Regression Trees to evaluate the relationships between yield variations and agronomic factors, including soil pH, nitrogen, phosphorus, potassium fertilization, and levels of phosphorus, potassium, and magnesium in the soil. The study found a downward trend in Climatic Water Balance, highlighting the increasing influence of climate change on regional water resources. Crop yields responded positively to increased agricultural inputs, especially nitrogen. Optimal soil pH and medium phosphorus levels were identified as crucial for maximizing yield. The findings underscore the importance of tailored nutrient management and adaptive strategies to mitigate the adverse effects of climate variability on cereal production. The results provide insights for field crop research and practical approaches to sustain cereal production in changing climatic conditions.

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Evaluation of Novel Picolinamide Fungicides (QiI) for Controlling Cercospora beticola Sacc. in Sugar Beet

Studies were initiated to find new effective fungicides to use under field conditions to discover novel approaches for optimizing disease management in sugar beet crops. Cercospora leaf spot (CLS), a prevalent foliar disease in sugar beet crops worldwide, is caused by the fungal pathogen Cercospora beticola Sacc. This disease has become the most prevalent pathogen in sugar beet crops across nearly all European growing regions, including Hungary. The epidemic spread of this disease can cause up to 50% yield loss. The use of fungicides has been a cornerstone in managing CLS of sugar beet due to the limited efficacy of non-chemical alternatives. However, the emergence of fungicide-resistant strains of Cercospora beticola Sacc. in recent decades has compromised the effectiveness of certain fungicides, particularly those belonging to the QoI (FRAC Group 11) and DMI (FRAC Group 3) classes. Hungary is among the many countries where resistance to these fungicides has developed due to their frequent application. Picolinamides represent a novel class of fungal respiration inhibitors targeting Complex III within the Quinoine-Inside Inhibitor (QiI) group. Two innovative fungicides from this class, fenpicoxamid and florylpicoxamid (both classified under FRAC Group 21), were evaluated for their efficacy in managing CLS of sugar beet in Hungary during the 2020 and 2021 growing seasons. Both fungicides were applied as formulated products at various application rates and demonstrated superior efficacy in controlling CLS compared to untreated control plots and the reference fungicides difenoconazole and epoxiconazole. The results consistently demonstrated that all tested application rates of fenpicoxamid and florylpicoxamid effectively controlled CLS in sugar beet, exhibiting a clear dose–response relationship. Disease severity, as measured by the area under the disease progress curve (AUDPC), was significantly correlated with yield reduction but showed no significant association with root sugar content. Moreover, data from both study years indicated that picolinamide fungicides applied at a rate of 75 g ai/ha significantly outperformed difenoconazole (100 g ai/ha) in controlling the CLS of sugar beet. Additionally, higher application rates of picolinamides at 100–150 g ai/ha outperformed epoxiconazole at 125 g ai/ha in disease suppression. Fenpicoxamid is currently registered for use in cereals within Europe, and outside of Europe in Banana against Black Sigatoka (eff. Mycosphaerella fijiensis). Florylpicoxamid, while not yet registered in Europe, is undergoing approval processes in various countries worldwide for a range of crops and is continually being evaluated for potential market introduction. Additional details regarding the efficacy of florylpicoxamid against CLS in sugar beet were presented at ‘The 10th International Conference on Agricultural and Biological Sciences (ABS 2024, Győr-Hungary)’ in 2024.

Nano-fertilizers: The future of nutrient approaches for cereals

Nanotechnology offers a promising solution to address the nutrient demands of cereals, which are nutrient-intensive crops typically reliant on large amounts of inorganic fertilizers. These large quantities of nutrients are supplied to the crop by applying a huge amount of inorganic fertilizer as a soil application led to a decline in environmental systems, particularly affecting the soil health, lesser use efficiency of fertilizers and water resources contamination. In this context, nanotechnology serves as a potential solution. The huge demand for nutrients in cereal crops is met by nano-fertilizers. The nano-fertilizers are now playing a promising role in fulfilling the nutrient requirement of the crops by replacing the normal conventional fertilizers not only for cereals but also for other agricultural crops. The mechanism of action of nano-fertilizers is that they are smaller than the pore size of plant cell walls, allowing them to easily enter and reach the plasma membrane. Nano-fertilizers have 100 times greater surface area than conventional fertilizers, allowing them to easily interact with plant surfaces and increase the absorption of nutrients. Therefore, the nutrient-use efficiency of the crop is increased and shown a positive effect on the physiological and growth parameters of cereal crops, resulting in enhanced yield and improved drought tolerance. This review discussed, the effects and efficiency of nano-fertilizers on cereal crop growth and the correlation between physiological, qualitative, quantitative traits with nano-form of application in detailed manner. This review concludes that nano-fertilizers are the potential nutrient source for cereal crops like rice (Oryza sativa L.), wheat (Triticum aestivum L.), maize (Zea mays) etc. and they are going to be a promising resource for nutrient management in agricultural crops.

Achieving synergistic benefits through integrated governance of cultivated cadmium contamination via multistakeholder collaboration.

Rice serves as a vital staple food, but its accumulation of cadmium (Cd) has sparked widespread concerns regarding food safety and ecosystem security. Here, we conducted a seven-year systematic field experiment in the Xiangjiang River Basin of China, where an integrated governance framework (IGF) was established to ensure rice safety. The IGF, tailored to geographical zoning and pollution gradation, includes targeted soil treatments, crop management strategies, and stakeholder engagement. The quality of both the soil and the crop was improved, with a reduction in soil Cd availability of 36%, and a decrease in Cd in rice grain of 57-78%. This framework not only addresses multiple challenges but also supports sustainable development goals (SDGs 2, 3, 6, 9) by fostering comprehensive synergies among science, policy, and local community participation. Our findings provide empirical guidance for safe rice production in Cd-contaminated areas and provide solid scientific-driven decision support globally.

Molecular Biodiversity in Fusarium subglutinans and F. temperatum: A Valuable Tool to Distinguish the Two Sister Species and Determine the Beauvericin Chemotype

Fusarium subglutinans and F. temperatum are widely distributed maize pathogens recognized as distinct species with a species-specific chemotype based on patterns of mycotoxins. Recent comparative genomic analysis revealed that genomes of both species carry a complete beauvericin (Bea) biosynthetic genes cluster, but the key gene Bea1 in F. subglutinans is not functional likely due to a large insertion (NRPS22ins) and multiple mutations (SNP298 and SNP528). We used the recently published genome sequences for these species to develop PCR markers for investigating the distribution of three main mutations in the Bea1 gene in a large collection of strains of both species from around the world. We also designed a PCR assay for a rapid and reliable discrimination of both species in the evaluation of crop exposure to mycotoxins. Overall, our results showed that SNP528 was the most common mutation, followed by NRPS22ins and SNP298. Moreover, phylogenetic analyses suggest that non-synonymous SNPs have occurred first, and that the resulting inactivation of BEA production has caused the accumulation of other polymorphisms, including the NRPS22ins, in the entire gene-coding region. The screening for genetic differences between these species could guide future crop management strategies.

Molecular identification of Fusarium species in commercial vanilla and crop wild relatives in Colombia.

Vanilla is an economically important crop for low-lying humid tropical regions, but cultivated plants face serious phytosanitary problems. Fusarium wilt is a devastating disease affecting vanilla crops, caused by the fungal pathogens Fusarium oxysporum f. sp. vanillae (Fov) and F. oxysporum f. sp. radicis-vanillae (Forv), part of the F. oxysporum species complex (FOSC). We characterized 29 fungal isolates from a vanilla crop and crop wild relatives (CWR) using molecular (EF1-α and ITS-rRNA loci) and morphological traits. Fusarium was the predominant genus, followed by Colletotrichum and Clonostachys. Four Fusarium species were identified: F. oxysporum (37.9%), Fusarium solani (20.7%), Fusarium pseudocircinatum (13.8%) and Fusarium concentricum (10.3%). The latter three species were isolated only from CWR and may represent latent pathogens. Fov was isolated from both the crop and CWR, while a Forv-affiliated isolate was also found in a vanilla crop, marking the first report in the neotropical region. The EF1-α locus provided greater genotype resolution, as well as having reference sequences for Forv. However, the fungal barcode ITS locus is widely applied. We recommend the continued use of both loci for Fusarium diagnosis in vanilla to facilitate early detection and the development of effective integrated crop management strategies.

A Lightweight Cotton Field Weed Detection Model Enhanced with EfficientNet and Attention Mechanisms

Cotton is a crucial crop in the global textile industry, with major production regions including China, India, and the United States. While smart agricultural mechanization technologies, such as automated irrigation and precision pesticide systems, have improved crop management, weeds remain a significant challenge. These weeds not only compete with cotton for nutrients but can also serve as hosts for diseases, affecting both cotton yield and quality. Existing weed detection models perform poorly in the complex environment of cotton fields, where the visual features of weeds and crops are similar and often overlap, resulting in low detection accuracy. Furthermore, real-time deployment on edge devices is difficult. To address these issues, this study proposes an improved lightweight weed detection model, YOLO-WL, based on the YOLOv8 architecture. The model leverages EfficientNet to reconstruct the backbone, reducing model complexity and enhancing detection speed. To compensate for any performance loss due to backbone simplification, CA (cross-attention) is introduced into the backbone, improving feature sensitivity. Finally, AFPN (Adaptive Feature Pyramid Network) and EMA (efficient multi-scale attention) mechanisms are integrated into the neck to further strengthen feature extraction and improve weed detection accuracy. At the same time, the model maintains a lightweight design suitable for deployment on edge devices. Experiments on the CottonWeedDet12 dataset show that the YOLO-WL model achieved an mAP of 92.30%, reduced the detection time per image by 75% to 1.9 ms, and decreased the number of parameters by 30.3%. After TensorRT optimization, the video inference time was reduced from 23.134 ms to 2.443 ms per frame, enabling real-time detection in practical agricultural environments.

Mapping Rice Paddy Areas in Nueva Ecija Using Temporal Sentinel -1 SAR Imagery

Abstract. Remote Sensing is proven to be helpful in various ways, and for an agricultural country like the Philippines, mapping in farmlands is not that common. Using the Sentinel 1 Synthetic Aperture Radar (SAR) data, the study reveals rice paddy classification brought by supervised machine learning on the municipality of Guimba, in Nueva Ecija - the Rice Bowl Capital of the country. The study in Guimba, Nueva Ecija, addresses the absence of comprehensive rice classification studies, focusing on creating a classified rice paddy map and assessing spectral indices (NDMI, NDVI, NDWI) using synthetic aperture radar and optical imager. Results show successful differentiation between rice and non-rice paddies. Temporal analysis emphasizes monitoring water availability, soil moisture, and vegetation health. Despite signs of overprediction in the CART model, its effectiveness in mapping rice paddies is notable. The spatial distribution maps contribute to targeted monitoring, enabling efficient interventions and improved agricultural practices. This research highlights the inherent use of remote sensing in rice crop management, offering valuable insights for farmers and country's food security.

Improving Rainfall Prediction Accuracy in the USA Using Advanced Machine Learning Techniques

The key aim of this research project is to design and evaluate advanced machine learning models for increasing accuracy in rainfall forecasting over the USA. We intended to investigate nonlinear relationships typical of the atmospheric variables using state-of-the-art ML methods for more accurate rainfall predictions. For this research project on rainfall forecasting in the USA, we utilized an extensive dataset that comprises historical rainfall data collected from the National Oceanic and Atmospheric Administration (NOAA) and other meteorological agencies. The main dataset we use in this paper consists of daily rainfall measurements across various geographical locations of the USA, thus capturing the wide-ranging historical data necessary for both training and validation of the model. Besides measuring rainfall, we included meteorological data from sources such as NOAA's Global Historical Climatology Network and NASA's Modern-Era Retrospective Analysis for Research and Applications. These datasets further provided key variables that are known to affect rain, including temperature, humidity, wind speed, and atmospheric pressure. The performance metrics used in this research work for the models considered include accuracy, precision, recall, and F1 score. The above table shows that the Random Forest Classifier outperformed the other models, achieving perfect accuracy. That indicated that it rightly classified all the instances in the test set. The Logistic Regression and Support Vector Machine models gave a quite good performance by giving above average accuracy but had lower precision and recall for the rainfall prediction. Accurate rainfall forecasting has direct consequences on agriculture, greatly empowering farmers and agricultural planners to make more effective decisions regarding planting, harvesting, and crop management. The forecasts of rainfall are also of critical importance in disaster management regarding planning for flood emergencies. Moreover, precise forecasting of rainfall, particularly in sustainable water resources management, presents the most important data in planning for and conserving these resources.

District Level Crop Weather Calendars and Advisories for Kharif Rice in Odisha, India

The crop weather calendar is a visual depiction of key facts about crop growth phases, typical crop water requirements, and alerts that should be sent out in response to weather that is conducive to the spread of pests and diseases. Farmers and other stakeholders may successfully plan crops, schedule irrigation, and take plant protection measures by using these calendars, which are practical and helpful tools for farmers. The crop weather calendars are prepared in different languages for better understanding to farmers and explain the usage importance and plan farm operation and activities by taking decisions as per the prevalent crop weather conditions in fields. It plays a vital role for important crops and various varieties through research & development-based process at village/ panchayat level. It also helpful for improving the quality of medium range weather forecast based on Agro-Advisory Services (AAS). The AAS provides strategies based on weather for crop and livestock management to increase production and food security. The AAS has been useful to advise farmers regarding farm operations to manage the inclement weather for effective crop production in the changing environment. The Research was carried out on the studies of crop weather calendar on Rice crop in different districts of Odisha by All India co-ordinated research project on Agrometeorology, Bhubaneswar centre with the support of Agro Meteorological Field Unit (AMFU). It has been prepared with an objective to study climate normal of past 30 years from (1993-2023). Climatic data of Rice crop in Kharif season has been taken 25th SMW to 42nd SMW for medium duration (125 days) and 26th to 49th SMW for long duration (150 days) Rice crop in both English and Odia (local) languages for the coastal districts i.e (Khordha, Puri, Kendrapada and Jagatsinghpur) of Odisha. From the crop weather calendar the medium duration of rice crop, it was found that for sowing to seeding the mean temperature is 290C, before the transplanting and the water requirement of 670-690 mm is found congenial for better yield of the crop. The growth of the crops severely affected where the temperature falls below 200C, for high yielding of Rice crop during the kharif season the favourable weather conditions are Tmax: 300C, Tmin: 260C, Rainfall: 78 mm to 110 mm during the 29th to 30th week with RH 87%. For the pest likes stem borer and leaf folder the congenial weather conditions are Temperature :20 to 300C, RH:87%, Wind speed (Km/h) <5 and Rainfall: 00-30 mm. Similarly for Blast and BLB diseases, the favourable conditions during the 28th-31st week to 48th week, when the temperature range 20-300C, RH=> 90 %, Wind speed < 5 (Km/h) RH:30-50%. It has been found that the severity of pest and diseases increases with decreases in temperature and High RH and very low rainfall.

AGROTECH: A Smart Agricultural System Using IOT and ML

Agriculture remains one of the most important sectors that support human survival which supplies basic needs, a source of income for many dependants' community. Despite these advantages, resource scarcity, adverse environmental conditions and pest infestations remain serious threats to crop products. To mitigate these concerns, we present a smart agriculture system which uses the most modern technologies like IoT (Internet of Things) machine learning (ML) and sensors with automation. The system consists of an intelligent irrigation mechanism for efficient utilization of water, an animal detection system used to trace cattle, and a Light Dependent Resistor (LDR) Sensor joined with buzzer for early detection of any global environmental changes. Additionally, the integration of plant disease detection capabilities further enhances the system's effectiveness. By employing various sensors to collect data on environmental factors, including moisture and temperature, the proposed framework enables timely decision-making for crop management. The aim is to provide farmers with actionable insights, ensuring food security while minimizing resource consumption and economic losses. This review paper discusses the potential benefits and implementation strategies of such an intelligent agricultural system, emphasizing the need for innovation in agricultural practices to meet the growing demands of an increasing population

Introducing intermediate wheatgrass as a perennial grain crop into farming systems: insights into the decision-making process of pioneer farmers

The perennial grain intermediate wheatgrass (Thinopyrum intermedium, commercial name KernzaTM) has been proposed as a diversification crop for producing forage and grain and providing ecosystem services to farmers. Although a few studies have addressed farmers’ interests in the crop, information is lacking about the links between farmers’ goals and crop management, i.e., how farmers aim at integrating this crop in their systems. Closing this gap, this paper analyzes for the first time the introduction of intermediate wheatgrass (IWG) from a farmer perspective, as a set of decision plans and goals. The overarching orientations of the farm and organization of the production system, referred as strategic decisions, interact with short-term crop management (i.e., tactical decisions) and farmers’ goals for IWG. In total, 17 individual semi-structured interviews and 2 collective crop management prototyping workshops in France were used to analyze farmers’ rationales as a function of their farm systems, agronomic constraints, and know-how. The study demonstrates that farmers’ interests in IWG revolved around multiple ecosystem services and financial returns. Three ideal-types of farms testing IWG emerged from the relationships between existing farming systems and goals for IWG. The strategic and tactical decisions regarding the integration and management of IWG were contingent on the farming systems, the goals for IWG, the farmers’ know-how, and their ability to mitigate risks. Implications for the future development of intermediate wheatgrass as a niche innovation are considered based on farmers’ points of view. This study provides insights into the ideas and concerns of French farmers regarding IWG and proposes a framework for discussing the introduction of a new crop in a farm system.

Inter-row cover crops influencing the development of conilon coffee

The use of forage as a cover crop is an alternative for the sustainable management of conilon coffee (Coffea canephora Pierre ex Froehner) crops. The objective of this study was to evaluate the herbage accumulation and nutritive value of forages used as cover crops and their effect on the productivity and physiology of conilon coffee plants. The inter-row management assessed were 1- Congo grass [Urochloa ruziziensis (R. Germ. & C.M. Evrard) Crins], 2- Mombaça guineagrass [Megathyrsus maximus (Jacq.) B.K. Simon & S.W.L. Jacobs], 3- Marandu palisadegrass [Urochloa brizantha (Hochst. ex A.Rich.) R.D.Webster], 4- weeds, 5- weeding and herbicide application. The experiment was conducted in 2020 and 2021 using a randomized block design (split-plot) with four replications and a plot size of 24 m2. Herbage accumulation of Congo grass, Mombaça guineagrass and Marandu palisadegrass (1.12 to 3.81 t/ha) were higher than weeds (0.18 to 1.95 t/ha) in seven periods evaluated. Mombaça guineagrass had the highest average herbage accumulation (1.47 to 3.81 t/ha). The forage cover crops did not differ among themselves for dry matter concentration, crude protein and C:N ratio in three periods evaluated. The inter-rows management with cover crops did not reduce productivity, grain/fruit ratio, grain size, vegetative vigour and physiology of the coffee plants compared to the management with weeding and herbicide in 2021. In 2022, they stagnated or reduced productivity by up to 49%, with changes in plant physiology. Adjustments in the management of cover crops are needed for the development of competitive and sustainable coffee crops.

Building Agricultural Resilience: Strategies for Climate Change Adaptation

Climate change has a significant impact on India's agriculture, influencing environmental conditions, agricultural techniques, and production. The term "climate-resilient agriculture" refers to technologies, methods, tools, and frameworks aimed at adapting to and mitigating the effects of climate change on agricultural production, food security, and livelihoods. Crop productivity and food security are greatly impacted by climate changes, which calls for creative agricultural methods to maintain food supply while reducing environmental damages. These difficulties are made worse by environmental changes and global population expansion, especially in developing countries. Technological advancements, such as geospatial analysis, and eco-friendly farming technologies, play a crucial role in optimizing land use and enhancing food security. Crop variety adaptation and management of seed, water, land, livestock, soil and nutrients, pest management, risk management, and post-harvest management are examples of effective agricultural adaptation strategies. Technological developments like geographic analysis.

Conservation Agriculture and Crop Residue Management

India being a populous country the intensification of the cropping system is mandatory. However, this intensification of the cropping system results in the degradation of soil and other natural resources. Considering this situation, conservation agriculture is the most suitable alternative to achieving sustainable yield and productivity. Conservation agriculture is based on three major principles those are minimum disturbance of soil, crop rotation, and maintenance of crop residue. Crop residue management plays a major role in conservation agriculture as it helps in improving soil productivity, soil organic matter content, soil structure, soil water conservation, air quality, and reduction in soil erosion. India produces 273 Mt of crop residues annually which contain a total of 7.16 Mt of nutrients (N-1.28, P2O5-1.97, K2O-3.91). In modern agriculture, green manure crops like Sesbania and sunhemp increase the quantity and quality of the crop residues in the crop field. Among various residue management practices mulching has been practiced prominently in dry lands as it increases the resource use efficiency. The residue decomposition is affected by various factors like quality of crop residue, edaphic, management, and climatic factors. In modern scientific research crop residues are being used especially for site-specific nutrient management. It is observed in most studies that zero tillage or reduced tillage with crop residue gives comparatively higher net returns, water use efficiency, and resource use efficiency than conventional agriculture. In Indian context. The management of rice residue particularly in the Indo-Gangetic Plains of India is challenging, where the rice-wheat cropping system is intensively followed and the farmers are more anxious about timely seeding of wheat in combine harvested paddy fields (Sidhu et al., 2007; Singh et al., 2020).

Assessment of cover crop adoption and impact on weed management in Wisconsin corn‐soybean cropping systems

AbstractThe evolution and widespread occurrence of herbicide‐resistant weeds pose a major challenge for farmers and crop consultants across North America, warranting integrated management strategies. The adoption of cover crops offer weed suppression and soil health benefits but adds management complexity. A survey, targeting farmers and crop consultants, was conducted during the spring of 2023 to evaluate current cover crop management practices and perceptions in Wisconsin cropping systems. The survey included 26 questions across five sections: respondent profile, cover crop adoption and experience, cover crop management ahead of soybean [Glycine max (L.) Merr.], cover crop management ahead of corn (Zea mays L.), and general benefits and challenges of cover crop adoption. Farmers and crop consultants represented most respondents, influencing a total of 29,500 and 557,000 ha, respectively. Waterhemp (Amaranthus tuberculatus [Moq.] J.D. Sauer) and giant ragweed (Ambrosia trifida L.) were identified as the most troublesome weeds. Over 90% of respondents use herbicides to terminate their cover crops, and 68% of respondents agree that cover crops improved overall weed control in their farms/clients’ farms to some extent [cereal rye (Secale cereale L.) as main cover crop species]. Additionally, results demonstrate a strong consensus (85%) on cover crops positively influencing water retention, with 98% agreement on their efficacy in reducing soil erosion. Results from this survey can help guide farmers, agronomists, researchers, and policymakers with cover crop adoption, management, policies, incentives, and future research and education needs in Wisconsin and beyond to support the development of more sustainable and efficient weed and crop management strategies.

The Brazilian case as a beacon to increase crop production in sub-Saharan Africa

Abstract Maize is one of the major agricultural commodities in the world, and a source of food in Africa, representing more than 40 million ha currently harvested on the continent. Despite sub-Saharan Africa's dependence on grain, the maize actual yield (Ya) of the crop is low when compared to its potential yield. In Brazil, the yield-gap between Ya and water-limited yield (Yw) is approximately 50% of Yw. The objective of this study was to carry out a case study, using upland maize as a reference to identify a set of agricultural areas with similar soil and climate in Brazil and sub-Saharan Africa (SSA). The climatic similarity between Brazil and SSA countries was verified, seeking homogeneous climatic zones that occur in both regions. The Ya was determined including the data of at least the last three years of cultivation and were taken from the database of the national institutes of agricultural statistics. The climatic data showed that the SSA had well-distributed rainfall throughout the crop season, being higher than in Brazil, as well as the average air temperature. The average Yw was 11.3 and 7.4 Mg/ha for Brazil and SSA, respectively. Maize Ya in SSA was 1.4 Mg/ha, while in Brazil Ya was 5.2 Mg/ha. Ya represented approximately 9% of Yw in the SSA. The low Ya shows the large yield-gap found in SSA. With this, it is evident that the technologies used and the crop management are largely responsible for the yield differences between Brazil and SSA.

AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

This article explores the multifaceted applications of Artificial Intelligence (AI) technologies in precision agriculture, focusing on their potential to significantly enhance crop yields while optimizing resource utilization. The article examines five key areas where AI is making substantial impacts: predictive analytics for crop management, intelligent irrigation systems, automated pest and disease detection, precision fertilizer application, and robotic harvesting. By integrating data from various sources and employing advanced machine learning algorithms, these AI-driven systems demonstrate remarkable improvements in efficiency, accuracy, and sustainability. The article highlights significant advancements, such as a 15% improvement in yield prediction accuracy, up to 30% reduction in water usage, and a 20% decrease in fertilizer use without compromising crop yields. While acknowledging challenges such as data privacy concerns and initial investment costs, the article underscores the long-term benefits of AI adoption in agriculture, including increased profitability, environmental sustainability, and improved food security. This comprehensive analysis provides insights into how AI-driven precision agriculture is reshaping modern farming practices and its potential to address global food production challenges while reducing agriculture's environmental footprint.