Agricultural Production Research Articles

Occurrence and Fate Analysis of Mycotoxins in Maize During the Post-Harvest Period

The consumption of agricultural products contaminated with mycotoxins poses a significant threat to the health of both humans and animals. Maize frequently becomes contaminated with toxic fungi while it is still growing in the field. Therefore, more proactive measures should be implemented to reduce mycotoxin levels during the storage and processing of maize after harvest. This article analyzes the prevalent mycotoxins found in maize, specifically aflatoxins, ochratoxins, trichothecenes, fumonisins, and zearalenone. The study provides a comprehensive analysis of the occurrence of mycotoxins in maize during storage, as well as fate analysis of them during processing. It summarizes the impacts of storage time, environmental conditions, storage methods, and agricultural practices on mycotoxin occurrence during storage in the post-harvest period. Furthermore, the different distribution of mycotoxins across various fractions during both dry- and wet-milling processes in the post-harvest processing period is analyzed. Additionally, the strategies to control mycotoxins in maize are also proposed during the post-harvest period. This review offers valuable insights for future research on mycotoxin contamination in maize during the post-harvest period.

REPORT OF PRACTICAL CLASS IN AGRICULTURAL AREA WITH THE BACHELOR CLASS IN AGRONOMY IN THE MUNICIPALITY OF TRACUATEUA-PA

Cassava (Manihot esculenta Crantz) is a highly consumed root and a significant source of carbohydrates in Brazil and around the world. In the state of Pará, cassava is vital to the local economy, especially in the municipality of Tracuateua. The culture involves family farmers and serves as an important source of income and subsistence. Different cultivars are used, such as BRS Tapioqueira and BRS Verdinha, which exceed 70 thousand kg/ha with good management. Adequate fertilization, pest control and weed management are essential for high productivity. Irrigation can significantly increase production. On the property of Mr. Benedito Dutra, member of the RENIVA project, the production of seed cassava and cowpea is highlighted. It uses cultivars developed by Embrapa, such as BRS Kiriris, BRS Formosa and BRS Novo Horizonte, which are recommended for industrial use. Dutra also grows cowpeas, using varieties such as BRS Guirá and BRS Natalina, which are highly productive and adapted to local conditions. The property has an irrigated area of ​​23 hectares per central pivot, ensuring crop hydration during dry periods. During the field visit, several areas of experimentation were observed. The experimental plots of cassava and cowpea showed good performance, with a high survival rate and resistance to pests. The integration of advanced soil management, pest control and irrigation practices highlighted the property as an example of innovative agriculture. The experience on Mr. Dutra’s property demonstrated the importance of innovation and adoption of advanced agricultural practices for sustainable and productive agricultural production.

Machine Learning-Based Crop Suitability Prediction: An Emerging Technique for Sustainable Agricultural Production in the Desert Region of India

ABSTRACT Machine learning (ML) algorithms can be applied to predict the suitability of soil for crop cultivation based on digital soil mapping. We used three distinct models viz. Multinomial Logistic Regression (MnLR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to predict the suitability of wheat and pearl millet in the Barmer district of the Thar Desert. After the computation of crop suitability classes, ML techniques were used to develop suitability maps of wheat and pearl millet in the study area. The study found that the RF and XGBoost models worked well to classify crop suitability. The RF model showed that 11.9% of the total area was highly suitable, 1.6% was moderately suitable, 14.9% was marginally suitable, and 71.6% was not suitable for wheat crop. RF model for pearl millet showed that 15.5% of the area is highly suitable. Soil suitability mapping for wheat showed a Kappa index ranging from 0.23 to 0.57 and an overall accuracy ranging from 0.79 to 0.86, whereas the prediction of suitability for pearl millet showed a moderate range of Kappa index from 0.31 to 0.58 and accuracy from 0.63 to 0.77. The area under curve (AUC) for wheat crop was 0.72, 0.88, and 0.91 for MnLR, RF, and XGBoost models, respectively. Overall, the RF model performed better than the MnLR model, showing a 16% increase in accuracy. Therefore, the developed suitability maps using ML provide valuable details on agricultural potential in the Indian desert region while harmonizing its impact on the environment and the economy.

High removal of volatile organic compounds on hierarchical carbons prepared from agro-industrial waste of banana fruit production for air decontamination.

Activated carbons were prepared from residues from agro-industrial banana production (banana pseudostem) and evaluated in the capture of five different volatile organic compounds (VOCs): dichloromethane, chloroform, ethyl acetate, hexane, and cyclohexane. The biomass was first submitted to a hydrothermal treatment in the presence of KOH or ZnCl2 as activating agents, followed by a dry pyrolysis. This new advance in methodology contributes to producing activated carbons with hierarchical porosity and high surface areas (701-1312 m2g-1), promoting increased interest in managing waste from banana fruit agricultural production. VOC capture studies were performed by thermal analysis, and capture capacities were similar to or higher than those presented in the literature. Higher adsorption capacities were related to the amount of available micropores, and the capture capacity was enhanced by the contribution of small mesopores. As the highest adsorbed amounts of dichloromethane (933mgg-1 at 25°C) were obtained for the material activated with ZnCl2 (1:3), further studies were carried out for this system. The experimental data was fitted using a pseudo-first-order kinetic model. A study was carried out in different atmospheres (He, N2, air), showing that co-adsorption is occurring. Under simulated environmental conditions, the capture capacity decreased slightly at equilibrium, and the new adsorbent was used for up to ten cycles without significantly losing its efficiency, indicating good application in the field.

Identifying rice lodging based on semantic segmentation architecture optimization with UAV remote sensing imaging

Lodging, a prevalent issue during rice growth, detrimentally impacts both yield and quality. It also complicates the harvesting process, reducing the efficiency of mechanized collection. Existing monitoring methods, predominantly based on manual observation and satellite remote sensing, fall short in addressing the requirements of contemporary, efficient, and real-time agriculture. This research integrates image analysis techniques with advanced optimization algorithms to develop a semantic segmentation model specifically designed for detecting rice lodging in remote sensing images. The model, named MI-UConvNeXt, employs a ConvNeXt-based feature extraction network utilizing UNet architecture (UConvNeXt) and incorporates an improved multi-objective salp swarm algorithm with Latin hypercube sampling and an elite opposition-based learning strategy (ISSA-LE) to dynamically adjusting the number of UConvNeXt channels. MI-UConvNeXt achieves a balance between accuracy and complexity. Compared to seven other semantic segmentation models from the literature, MI-UConvNeXt exhibits enhanced performance, with a Pixel Accuracy (PA) of 95.59%, mean Pixel Accuracy (mPA) of 95.62%, and mean Intersection over Union (mIoU) of 91.91% on the validation set. This demonstrates the model’s superior accuracy, lower computational resource demands, and enhanced efficiency. By integrating deep learning with intelligent optimization algorithms, this study offers a novel and effective approach for monitoring crop lodging in agricultural production, providing robust technical support for the accurate extraction of crop phenotypic information.

Collaborative Development of Cross-border E-commerce and Agricultural Product Supply Chain: Opportunities, Challenges and Solutions

Abstract: The development of science and technology and the deepening of globalisation have opened up new directions for developing the agricultural industry. With the gradual expansion of the scope of Internet +, traditional trade has gradually evolved into e-commerce, and relatively simple domestic trade has gradually expanded into cross-border transactions. With the help of e-commerce platforms, sales channels can be expanded, and the share of agricultural exports can be increased. The development of cross-border e-commerce is highly valued and mutually supportive of the agricultural product supply chain (APSC), which promotes the international trade of agricultural products. Optimised APSC provide high-quality produce to meet consumer demand. Technological innovations such as big data and the Internet of Things (IoT) bring smart and efficient optimisation solutions to APSC, improving the efficiency of product distribution. This paper reviews the research on cross-border e-commerce and APSC by some scholars in recent years, summarises the current development of cross-border e-commerce, describes the problems of APSC, and proposes solutions. Reviewing and summarising the relevant research literature aims to help scholars understand the development of cross-border e-commerce and APSC more systematically and comprehensively, and to provide strong support for future research and practice.

Sustainable agriculture development for food safety and nutrition

Sustainable agriculture is a type of agriculture which, in addition to producing food and agricultural products, is also economically advantageous for farmers, socially correct, respectful of nature and the environment. Sustainable agriculture contributes to improving the quality of life of both farmers and society. Climate change, economic development and social equity influence the sustainable agriculture and require complex and shared solutions on a large scale. Sustainable agriculture includes different meanings, such as environmental, social and economic perspective. The environmental perspective is related to the development of virtuous processes that respect the planet's resources, the healthiness of the soil and water, guaranteeing the maintenance of biodiversity and avoiding the use of pesticides and chemical agents. The social and economic perspective is the ability to meet the global demand of both industrialized and developing countries by ensuring the improvement of the protection of human rights and solidarity-based economic development, the quality of life of farmers, the ethical treatment of animals. The aim of this work is to discuss the implications and perspectives of sustainable agriculture in relation to food production.

The carbon emission reduction benefits of the transformation of the intensive use of cultivated land in China

The transformation of the intensive use of cultivated land has an important impact on agricultural carbon emissions (ACE). However, the existing research on the impact mechanisms of the transformation of intensive use of cultivated land on carbon emissions is not clear. In view of this, this study constructs the impact mechanism of the transformation of intensive use of cultivated land on carbon emissions from the perspective of production factors substitution based on the theory of induced technological change and the theory of marginal returns. Using the Cobb-Douglas production function, this study identifies the transformation patterns of intensive use of cultivated land in China from 1980 to 2021. Furthermore, this study simulates the carbon emission reduction benefits brought about by the transformation of the intensive use of cultivated land by considering the cost of agricultural production. The results show that, first, from 1980 to 2021, the price of agricultural labor increased by 224.10%, while the price of other agricultural production factors significantly decreased. China’s intensive use of cultivated land methods has undergone four stages: agricultural labor–intensive (ALI), yield–enhancing factor–intensive (YEFI), labor–saving factor–intensive (LSFI), and innovation factor–intensive (IFI). In 2021, most of China’s provinces were in the YEFI or ALI stages. Second, between 1993 and 2021, the transformation of China’s intensive use of cultivated land brought about an 87.48% increase in carbon emissions, while the spatial clustering effect of carbon emissions weakened. Furthermore, the results show that, the transition of the use of cultivated land from ALI to YEFI and YEFI to LSFI will reduce carbon emissions by 7.89%, and input factors costs will increase by 23.85%. If all regions, except for those in the IFI stage, transition to the LSFI model, it will result in a 17.56% reduction in carbon emissions, with a corresponding increase of 33.40% of the input factors input costs. This research enriches the theoretical foundation of literature about the transformation of the intensive use of cultivated land. It also expands the research perspective on agricultural carbon emissions. This research also helps provide references for the direction of the transformation of the intensive use of cultivated land in China and the achievement of the dual carbon goals.

Evaluation of the Quality of Organic Compost Produced From Fish Filleting Waste: Application and Use in Agriculture

Objective: This study aims to chemically characterize the compost resulting from fish filleting waste, identifying macro and micronutrients, and investigating the effects of different dosages of this compost on lettuce fertilization. Theoretical Framework: The research is based on concepts of sustainable management of urban solid waste, highlighting composting as an effective practice for recycling organic matter. Previous studies on composting animal waste and its application underpin the context of the research. Method: The methodology consisted of experiments with 28 plants, applying 7 treatments with 4 replications, ranging from 10% to 50% fish compost. Fresh and dry mass, root size and volume, and nutrients were analyzed. Results and Discussion: The results indicated that fish waste compost has significant potential as a source of nutrients, with medium to high levels of macro and micronutrients. In lettuce cultivation, the 10% dosage was the most cost-effective, although proportions of up to 30% promoted good development in all the indicators evaluated. Research Implications: The practical implications of this research suggest that fish waste compost could be a viable alternative for organic fertilization, promoting sustainable practices in waste management and agricultural production. Originality/Value: This study contributes to the literature by exploring the use of fish filleting waste compost as a fertilizer, demonstrating its effectiveness and potential as a source of nutrients for lettuce cultivation.

Sentiment and success potential of farmers’ producer organizations: A systematic literature review

Farmer Producer Organisations (FPOs) are a viable alternative to cooperative farming, offering a commercialization model and improved income for smallholders. However, their adoption is often met with mixed sentiment among farmers, who lack capital, technical expertise, and institutional innovations. This study aims to explore the sentiments related to the adoption and success of FPOs in establishing a beneficial platform for small and marginal farmers, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), sentiment and thematic analysis. The findings suggest that FPOs can enhance marketing strategies and agricultural productivity, but their impact on agriculture is case-sensitive. Cooperatives, characterized by member ownership and bottom-up development approaches, are essential factors in the context. A programmatic and comprehensive understanding of FPOs can help to develop a better agricultural and allied business ecosystem for diversified categories of farmers. Study aims to assess the sentiments and success potential of FPOs under an abundance of small and marginal farmers, comparing them to cooperative and contract farming legislation, to encourage a sustainable agribusiness ecosystem in India.

Effects of Land and Labor Costs Growth on Agricultural Product Prices and Farmers’ Income

Widespread attention has been paid to the continuous rounds of rising agricultural product prices in China since 2003. During this period, rising prices were affected by input costs and international market prices; therefore, the degree of benefits obtained by farmers from the rise in agricultural product prices has been questioned. However, limited studies have examined the effect and relationship between rising agricultural product prices and farmers’ income. The purpose of the study was to provide empirical evidence on how the rise in agricultural product prices affects farmers’ income and to understand the role of land and labor costs in this context using provincial data from 2003 to 2020. The findings reveal that there is a threshold effect on the impact of the rise in agricultural product prices on farmers’ income. A moderate rise in agricultural product prices can increase farmers’ income, but excessive price increases have an insignificant effect. The causes behind the price rise significantly influence farmers’ income. An increase attributed to higher land and labor costs can improve farmers’ income, whereas a rise driven by international market prices or the cost of agricultural production inputs is unlikely to benefit farmers.

Water productivity and date palm production: a field study in Biskra Region

In southern Algeria, agriculture is mainly based on groundwater irrigation, leading to significant water consumption, Improving water productivity in field-scale irrigation is critical for sustainable water management and the protection of this valuable resource. In the present study, to analyze water productivity, data were collected on the volumes of irrigation water applied and the resulting yields from 24 farmers through a survey questionnaire during the 2022-2023 agricultural season water productivity for date palm cultivation was evaluated in Biskra region of Algeria. The study found that water productivity values ranged from 0.4 to 1.1 in kg m-3 with an Average of 0.83 kg m-3, consequently, producing one kilogram of dates requires 1220.35 liters of water. Based on the findings of this study, it can be concluded that the water productivity of date palms was moderate compared to the results of other studies conducted in various regions around the world. This research highlights the critical need for strategies to enhance water-use efficiency, including the implementation of advanced irrigation technologies and improved management practices.

Utilizing large‐scale insurance data sets to calibrate sub‐county level crop yields

AbstractCrop yields are crucial for research on agricultural risk and productivity but are typically only available at highly aggregated levels. Yield data at more granular levels of observation have the potential to enhance econometric identification and improve statistical power but are typically inaccessible. Crop insurance contracts offered via the US Federal Crop Insurance Program (FCIP) are priced, in part, based on past yields of the farm meaning year‐to‐year variation in premium rates has the potential to provide insight into how yields vary over time. This paper introduces methods to use observed FCIP rating parameters to calibrate yields for insurance transactions lacking such data. These methods are validated with 148,243 farm‐level observations from Kansas for which yields are known. The calibrated yields are applied empirically to examine the impact of asymmetric information in the FCIP via choice of insurance unit structure and the extent to which legislative changes mitigated this effect.

Keypoint-Based Bee Orientation Estimation and Ramp Detection at the Hive Entrance for Bee Behavior Identification System

This paper addresses the challenge of accurately estimating bee orientations on beehive landing boards, which is crucial for optimizing beekeeping practices and enhancing agricultural productivity. The research utilizes YOLOv8 pose models, trained on a dataset created using an open-source computer vision annotation tool. The annotation process involves associating bounding boxes with keypoints to represent bee orientations, with each bee annotated using two keypoints: one for the head and one for the stinger. The YOLOv8-pose models demonstrate high precision, achieving 98% accuracy for both bounding box and keypoint detection in 1024×576 px images. However, trade-offs between model size and processing speed are addressed, with the smaller nano model reaching 67 frames per second on 640×384 px images. The entrance ramp detection model achieves 91.7% intersection over union across four keypoints, making it effective for detecting the hive’s landing board. The paper concludes with plans for future research, including the behavioral analysis of bee colonies and model optimization for real-time applications.

An Extended Method Based on the Geometric Position of Salient Image Features: Solving the Dataset Imbalance Problem in Greenhouse Tomato Growing Scenarios

Machine vision has significant advantages in a wide range of agricultural applications; however, acquiring a large number of high-quality image resources is often challenging in actual agricultural production due to environmental and equipment conditions. Therefore, crop image augmentation techniques are particularly important in crop growth analysis. In this paper, greenhouse tomato plants were used as research subjects to collect images of their different fertility stages with flowers and fruits. Due to the different durations of each fertility period, there is a significant difference in the number of images collected. For this reason, this paper proposes a method for balanced amplification of significant feature information in images based on geometric position. Through the geometric position information of the target in the image, different segmentation strategies are used to process the image and supervised and unsupervised methods are applied to perform balanced augmentation of the image, which is combined with the YOLOv7 algorithm to verify the augmentation effect. In terms of the image dataset, the mixed image dataset (Mix) is supplemented with mobile phone images on top of in situ monitoring images, with precision increased from 70.33% to 82.81% and recall increased from 69.15% to 81.25%. In terms of image augmentation, after supervised balanced amplification, the detection accuracy is improved from 70.33% to 77.29%, which is suitable for supervised balanced amplification. For the mobile phone dataset (MP), after amplification, it was found that better results could be achieved without any amplification method. The detection accuracy of the mixed dataset with different data sources matching the appropriate amplification method increased slightly from 82.81% to 83.59%, and accurate detection could be achieved when the target was shaded by the plant, and in different environments and light conditions.

Versatile MXenzymes Scavenging ROS for Promotion of Seed Germination under Salt Stress.

Salinization is recognized as a global problem, restricting agricultural production and sustainability. Targeting the salinity-induced oxidative stress, antioxidant treatment represents a protective strategy to improve plant salt tolerance. Herein, we report a V4C3 MXene nanozyme (MXenzyme), which exhibits good biocompatibility and excellent reactive oxygen species scavenging activity to ameliorate the salt stress-induced inhibition of seed germination. V4C3 MXenzyme treatment can significantly relieve salinity-induced oxidative stress and restore the antioxidant system in pea seeds, thus improving the phenotypic traits during germination. The molecular mechanism by which antioxidant V4C3 MXenzymes augment salt tolerances is revealed through transcriptomics and metabolomics. V4C3 MXenzymes significantly regulate the gene expression of antioxidant enzymes and molecule biosynthesis that correlate closely with hormone signal transduction genes and energy metabolism genes. With correlation and the combined analysis, redox homeostasis targeted by antioxidant V4C3 MXenzymes plays a critical role in promoting plant growth under salt stress.

Evaluating the Availability and Accessibility of Agricultural Extension Services Under the Single Spine System in Uganda. A Case of Smallholder Crop Farmers in Buhweju District

Purpose: This study evaluated the availability and accessibility of agricultural extension services under the Single Spine System, with a specific focus on smallholder crop farmers in Buhweju District. Methodology: The study adopted a mixed-methods approach, employing both quantitative and qualitative data collection techniques. Structured questionnaires were administered to a sample of 393 smallholder crop farmers and key informant interviews with district agricultural officers and extension workers provided in-depth insights into the delivery of services. Findings: The results indicate that education was positively and significantly (P<0.01) associated with knowledge about the availability of extension services. Farmers with more years of schooling had a greater likelihood of being aware of extension providers. Similarly, larger households with more labor resources (median size = 4) had better access to extension services. Moreover, household land size (median = 3 acres) and income (median = UGX 250,000) were significantly correlated with both availability and access to extension services, with wealthier farmers seeking more information to improve agricultural productivity. The study also revealed that proximity to extension service centers was a critical factor; households closer to sub-county offices (median distance = 1.5 km) had higher knowledge of available services. Access to credit and group membership were also major determinants of both awareness and access to extension services. Challenges were also identified, including limited outreach by extension officers, inadequate allowances, and insufficient logistical support for field officers. Unique Contribution to Theory, Policy and Practice: The study recommends increased sensitization efforts using local leaders, social media, and community networks, alongside greater support for sub-county agricultural officers to ensure effective service delivery.

Gender dynamics and aspirational disparities in agriculture

AbstractDespite increasing scholarly attention to individual aspirations, rural women's aspirations in agriculture remain largely unexplored. With women increasingly central to agricultural work, understanding their aspirations is crucial. Using farm surveys from Ecuador, Kenya, and Rwanda, we investigate the relationship between gender and aspiration formation in agriculture. Similar to previous findings related to education and entrepreneurship, our findings show women have lower ambitions for agricultural production. Addressing the unique challenges they face, gender‐sensitive policies are vital for inclusive, sustainable agriculture. Our study contributes to a broader understanding of the multidimensional factors shaping agricultural outcomes, underscoring the importance of integrating diverse perspectives for effective policy formulation in agriculture.

Advances in Nanotechnology for Sustainable Agriculture: A Review of Climate Change Mitigation

Currently, one of the main challenges is the mitigation of the effects of climate change on the agricultural sector. Conventional agriculture, with the intensive use of herbicides and pesticides to control weeds and pests, and the improper use of mineral fertilizers, contributes to climate change by causing increased greenhouse gases and groundwater pollution. Therefore, more innovative technologies must be used to overcome these problems. One possible solution is nanotechnology, which has the potential to revolutionize the conventional agricultural system. Active nanoparticles can be used both as a direct source of micronutrients and as a delivery platform for bioactive agrochemicals to improve crop growth, yield, and quality. The use of nanoparticle formulations, including nano-pesticides, nano-herbicides, nano-fertilizers, and nano-emulsions, has been extensively studied to improve crop health and shelf-life of agricultural products. Comprehensive knowledge of the interactions between plants and nanoparticles opens up new opportunities to improve cropping practices through the enhancement of properties such as disease resistance, crop yield, and nutrient use. The main objective of this review is to analyze the main effects of climate change on conventional agricultural practices, such as the use of pesticides, herbicides, and fertilizers. It also focuses on how the introduction of nanoparticles into conventional practices can improve the efficiency of chemical pest control and crop nutrition. Finally, this review examines in depth the last 10 years (2014–2024) of scientific literature regarding the use of nanoparticles in agriculture to mitigate the effects of climate change.

An integrated approach of SCOR and DEMATEL for risk assessment in the cold chain of agricultural products

An integrated approach of SCOR and DEMATEL for risk assessment in the cold chain of agricultural products