Individual Crops Research Articles

Enhancing sustainable Chinese cabbage production: a comparative analysis of multispectral image instance segmentation techniques

Accurate instance segmentation of individual crops is crucial for field management and crop monitoring in smart agriculture. To address the limitations of traditional remote sensing methods in individual crop analysis, this study proposes a novel instance segmentation approach combining UAVs with the YOLOv8-Seg model. The YOLOv8-Seg model supports independent segmentation masks and detection at different scales, utilizing Path Aggregation Feature Pyramid Networks (PAFPN) for multi-scale feature integration and optimizing sample matching through the Task-Aligned Assigner. We collected multispectral data of Chinese cabbage using UAVs and constructed a high-quality dataset via semi-automatic annotation with the Segment Anything Model (SAM). Using mAP as the evaluation metric, we compared YOLO series algorithms with other mainstream instance segmentation methods and analyzed model performance under different spectral band combinations and spatial resolutions. The results show that YOLOv8-Seg achieved 86.3% mAP under the RGB band and maintained high segmentation accuracy at lower spatial resolutions (1.33 ~ 1.14 cm/pixel), successfully extracting key metrics such as cabbage count and average leaf area. These findings highlight the potential of integrating UAV technology with advanced segmentation models for individual crop monitoring, supporting precision agriculture applications.

Climate Change as an Existential Threat to Tropical Fruit Crop Production—A Review

Climate change is an emerging threat to global food and nutritional security. The tropical fruits such as mango, bananas, passionfruit, custard apples, and papaya are highly sensitive to weather changes especially; changes of monsoon onset and elevated temperature are influencing crop growth and production. There is a need for more specific studies concerning individual crops and regional variations. Long-term effects and interactions of weather parameters and increased concentration of greenhouse gases, especially carbon dioxide, with phenological stages of the plant, pests, and diseases remain understudied, while adaptation strategies require further exploration for comprehensive understanding and effective mitigation. Few researchers have addressed the issues on the effect of climate change on tropical fruits. This paper focuses on the impact of abiotic (temperature, rainfall, humidity, wind speed, evaporation, carbon dioxide concentration) and biotic (pest and pathogens dynamics) factors affecting the fruit crop ecosystem. These factors influence flowering, pollination, fruit set, fruit yield and quality. This review paper will help develop adaptive strategies, policy interventions and technological innovations aimed at mitigating the adverse effects of climate change on tropical fruit production and safeguarding global food and nutritional security.

Spatial-temporal analysis and trend prediction of regional crop disease based on electronic medical records

Intelligent diagnosis of individual crop diseases has matured. How to understand the evolution patterns and predict regional disease trends remains a significant challenge. Plant Electronic Medical Records (PEMRs) offer valuable spatial-temporal characteristics about crop diseases, presenting a new opportunity for predicting the occurrence of regional diseases. In this study, we used a large prescription database from Beijing (2018-2021) to reframe regional disease prediction as a time series forecasting task. Firstly, to analyze spatial-temporal evolution patterns, we use ArcGIS to extract key information and identify potential connections between different disease occurrence points. Then, we developed a novel deep learning combined model SV-CBA, which combines Seasonal and Trend decomposition (STL) with Variational Mode Decomposition (VMD) to identify trend, seasonal, and residual components, and re-decomposes the residuals. STL-VMD can capture long-term trends and periodic variations while managing nonlinear and volatile characteristics. The CNN-BiLSTM-Attention model calculates disease trends by linearly integrating predictions of each sub-series. To reduce computational complexity while maintaining predictive performance, we propose an improved simplified attention mechanism. Our model demonstrates superior performance in both comparative and ablation experiments using the PEMRs dataset, outperforming numerous other models. This study provides accurate disease trend predictions, aiding farmers and regional managers in agricultural production management.

Do Crops Grown at Urban Dumpsites Contain Metals at Levels that Pose Unacceptable Health Risks to Consumers?

Excessive dietary metal intake from crops grown on contaminated urban dumpsites poses a global health risk to consumers. We evaluated the health risk to adult and child consumers from dietary exposure to metals and metalloids in crops cultivated at the Mbale (Uganda) dumpsite centre. Thirteen crop types grown on the dumpsite soil were sampled and analyzed for concentrations of 11 metals: Fe, Al, Zn, Mn, Cu, Pb, Cr, Hg, Co, Ni, Cd, and two metalloids: Se and As. Different proportions of the crops were combined into 12 meal classes to simulate the diets of residents and estimate noncancer and cancer risks. The findings indicated that most individual crop types and simulated diets lacked sufficient selenium for bodily functions. Furthermore, their metal accumulations exceeded the Food and Agriculture Organization (FAO) and the World Health Organization (WHO) permissible limits (mg/kg) for Al (20), Fe (100), Ni (10), Cu (20), Mn (10), Pb (0.3), Se (0.05), and Zn (99.4). The four most abundant metals in the various crop types and diets were Al, Fe, Mn, and Zn. A positive correlation between the metals in the crops indicated a common origin, which could possibly be the dumpsite soil. The chronic dietary intake (CDI) of metals was higher in children, and thus children faced higher noncancer and cancer risks compared with adults. The overall CDI values for each metal ranged from 0.000718 to 2.171 in adults, and 0.00125 to 3.781662 in children, which is approximately 1.74 times higher in children than in adult consumers. The noncancer and cancer risks ranged from moderate to high with Co, Cr, Fe, Mn, and Zn being mostly responsible for the high noncancer risks, and Al being the predominant contributor to cancer risks. The total noncancer risk levels equally ranged from moderate (1.4-3.3) for adults, and moderate to high (2.4-5.7) for children; the cancer risks were moderate to high in adults, with Al contributing to between 68% and 92% of the total risks across the 12 meal classes. Overall, CDI values and noncancer and cancer risks were all higher in children than in adults. The vegetables Amaranthus hybridus, Vigna unguiculate, Amaranthus dubius, and Cucurbita maxima significantly contributed to the high noncancer risk to both adults and children, particularly when they constituted 40% or more of the meal. Four additional vegetables (Cocorhrous olitorous, Brassica oleracea, Amaranthus cruentus, and Gynandropsis gynandra) also posed a high risk to children when consumed in large quantities. Our results highlight the urgent need to develop regulatory frameworks and/or rigorously enforce existing land and food governance policies to protect consumers' health from unsafe metal concentrations in crops grown on dumpsites. Environ Toxicol Chem 2024;00:1-17. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Evolution of global water footprints of crop production in 1990–2019

Abstract Crop production has the largest water footprint (WF) of all economic sectors and ranks as a leading cause of water scarcity. Despite this, our understanding of historical changes in global WFs of crops remains limited. In this study, we analyse the recently published dataset on green and blue WFs of 175 individual crops in 1990–2019. We explore the main changes in unit WFs (expressed in m3t−1 yr−1) and WFs of production (m3 yr−1) and connect the observed changes to various physical and socio-economic drivers. We find that nearly 80% of crops reduced global average unit WFs (required less water per tonne) as crop yields improved and cultivation centred around more productive areas. However, the total WF of crop production increased by 30% as these productivity gains were insufficient to compensate for cropland expansion of mostly water-intensive crops. Close to 90% of the increase occurred between 2000–2019 likely driven by accelerated economic growth, globalisation, changing diets, and production of first-generation biofuels. Among crops, we observe the largest increases for oil palm fruit, soya beans, and maize as they became the main providers of crop-based nutrients, animal feed, and biofuels for the modern economy. Among regions, most of the increase occurred across the tropics, mainly in Indonesia, Brazil, and Nigeria. However, India, China, and the USA had the largest WFs of production over the study period. Humanity consumed 6.8 trillion m3 of water (87.1% green) to produce crops in 2019. This number is likely to increase in the future which may exacerbate already existing environmental and socio-economic issues. Thus, it is important to transition to more water-sustainable agrifood systems. Much potential still exists in increasing crop water productivity, shifting production to less water-scarce geographies, and rethinking our dietary and industrial consumption patterns.

Soft yet secure: Exploring membrane buckling for achieving a versatile grasp with a rotation-driven squeezing gripper

Nowadays, there is a growing demand for versatile robotic grippers that facilitate adaptive grasping across a range of objects, characterized by diverse attributes such as shapes, sizes, and mechanical properties. This research introduces a new soft gripper, denoted as ROSE (ROtation-based Squeezing GrippEr), dedicated to the torsional buckling phenomenon to achieve its grasping capability. The inherent gripping pattern, formed through a single rotational actuation, enables ROSE to dynamically accommodate various objects (relatively smaller than the diameter of ROSE), even in challenging environments like an oil container, without a complicated controller. Experimental tests demonstrate that ROSE exhibits substantial gripping force, reaching up to more than 300 N in the specific setup, and a remarkable payload-to-weight ratio. Especially, ROSE can maintain mechanical integrity through long-term open-close operation cycles in a specific condition. In this paper, we also introduce non-linear simulations for the elaboration of behaviors of ROSE concerning different morphologies, encompassing geometry configurations and material properties. The findings highlight a significant correlation between morphological features and grasping performance, leading to the refinement of a dependable version of ROSE. This refined version was subsequently assessed through experimental trials in crop harvesting tasks, where ROSE demonstrated high success rates in picking both individual and clustered crops, regardless of their soft or stiff characteristics. Project’s website with videos: https://sites.google.com/view/rosesoftgripper .

Distribution, sources, and risk assessment of polycyclic aromatic hydrocarbons in surface soils and plants from industrial and agricultural areas, Junggar Basin, Xinjiang

The contamination characteristics of Polycyclic Aromatic Hydrocarbons (PAHs) in different environmental functional areas are different. In this study, the contamination of PAHs in soils and common plants in typical mining and farmland areas in Xinjiang, China, was analyzed. The results showed that the contamination levels of PAHs in mining soils were significantly higher than those in farmland soils, and the mining soils were dominated by 4-5-ring PAHs and farmland soils by 3-4-ring PAHs. Analysis of their sources using a positive definite factor matrix model showed that PAHs in mining soils mainly originated from coal and natural gas combustion, and transportation processes; while farmland soils mainly came from biomass and coal combustion, and fossil fuel volatile spills. The cancer risk of PAHs in soils was evaluated using a combination of the Monte Carlo and the lifetime carcinogenic risk models, and the results showed that the overall level of cancer risk for mining soils was higher than that for farmland soils, and can put some people in high risk of cancer. For plant samples, except for individual crop samples, the contamination levels of mining plants and crops were similar, with 4-5-ring PAHs dominating in desert plants in mining areas and the highest proportion of 3-ring PAHs in crops in agricultural fields, and PAHs in both plants were mainly from biomass and coal combustion. The results of correlation analysis showed that 2-ring PAHs in crop roots were significantly positively correlated with it in corresponding soils, and some high-ring PAHs in crop leaves were significantly negatively correlated with it in corresponding soils. Therefore, there were significant differences in the pollution characteristics of PAHs in soils and common plants in mining and agricultural areas. Human health risks and ecological risks are mainly concentrated in mining areas, and appropriate intervention measures should be taken for pollution remediation.

Customized Plant Growth Promotion with Soil- and Cultivar-Compatible Microbial Biofertilizers

Organic fertilizers and microbial biofertilizers are now widely recognized to effectively complement traditional mineral fertilizers for plant growth. The present study shows that bio-organic fertilizers can be enhanced by the addition of functional plant-growth-promoting rhizobacteria (PGPR) that provide additional benefits to plants. We hypothesized that not all beneficial soil bacteria are functional in different farm soils and plant varieties; hence, the most effective PGPR that are suitable to each farm’s individual cropping conditions were selected. Five different field soils and their respective crops were tested for compatibility with six microbial biofertilizers (including three new bacterial strains) to supplement a commercially available bio-organic fertilizer. In pot trials with lucerne plants, four out of the six microbial treatments led to significant (p < 0.05) growth promotion benefits (up to 79.8% more leaves and dry weight) compared to mock-treated or bio-organic fertilizer-only-treated control plants. A trial with industrial hemp demonstrated that compatibility with PGPR occurs in a cultivar-specific manner, leading to growth promotion ranging from −3.4% to 68.9%, with each cultivar displaying a preference for a different PGPR. Finally, pot trials with Rhodes grass and two different soils demonstrated high yield increases compared to control plants, with Bacillus amyloliquefaciens 33YE being most effective for one soil and Bacillus velezensis UQ9000N/Pseudomonas lini SMX2 for the other soil. Yield advantages reduced after several cuts of grass, but a repeat biofertilizer treatment at 69 days after the initial treatment restored high yield advantages, with the same PGPR again being most effective. These results demonstrate the importance of customization of microbial inoculants to identify the most compatible PGPR–cultivar–soil interaction. The customization of microbial biofertilizers to soils and plant cultivars, combined with complementary fertilizer applications, can potentially lead to more reliable and more sustainable agricultural practices.

Field-based multispecies weed and crop detection using ground robots and advanced YOLO models: A data and model-centric approach

The implementation of a machine-vision system for real-time precision weed management is a crucial step towards the development of smart spraying robotic vehicles. The intelligent machine-vision system, constructed using deep learning object detection models, has the potential to accurately detect weeds and crops from images. Both data-centric and model-centric approaches of deep learning model development offer advantages depending on environment and non-environment factors. To assess the performance of weed detection in real-field conditions for eight crop species, the Yolov8, Yolov9, and customized Yolov9 deep learning models were trained and evaluated using RGB images from four locations (Casselton, Fargo, and Carrington) over a two-year period in the Great Plains region of the U.S. The experiment encompassed eight crop species—dry bean, canola, corn, field pea, flax, lentil, soybean, and sugar beet—and five weed species—horseweed, kochia, redroot pigweed, common ragweed, and water hemp. Six Yolov8 and eight Yolov9 model variants were trained using annotated weed and crop images gathered from four different sites including combined dataset from four sites. The Yolov8 and Yolov9 models’ performance in weed detection were assessed based on mean average precision (mAP50) metrics for five datasets, eight crop species, and five weed species. The results of the weed and crop detection evaluation showed high mAP50 values of 86.2 %. The mAP50 values for individual weed and crop species detection ranged from 80.8 % to 98 %. The results demonstrated that the performance of the model varied by model type (model-centric), location due to environment (data-centric), data size (data-centric), data quality (data-centric), and object size in the image (data-centric). Nevertheless, the Yolov9 customized lightweight model has the potential to play a significant role in building a real time machine-vision-based precision weed management system.

Scientific achievements in Siberian agriculture and their development

A historical review of the formation and development of scientific farming in Siberia is given. The main achievements of the leading research institutes of the region are highlighted. The contribution of Siberian scientists to the development of scientific farming, including its constituent directions, is shown. It presents almost 100 years of development of research on agriculture - from the Omsk experimental farm (XIX century), agricultural experimental stations - to modern federal scientific centers. The evolution of farming from individual crop cultivation techniques to the formation of zonal and then adaptive-landscape farming systems and modern agro-technologies is demonstrated. Examples of completed scientific developments on farming, ready for implementation in production, the number of which runs into thousands, are given. Implementation of knowledge-intensive agro-technologies guarantees grain production in Siberia up to 25-30 million tons, sustainable development of farming, stable food supply and high return on non-renewable resources. The evolution of plant protection from individual methods against the most dangerous pests to the formation of integrated systems that take into account the phytosanitary state of agrocenoses and agro-ecological conditions of agricultural crops cultivation is shown. The most important techniques to optimize the relationship between the crop and pests are the selection of plant species and varieties resistant to their effects, optimization of mineral nutrition of plants and sowing dates. At present, the development of scientific farming is associated with the design of adaptive-landscape farming systems, selection and support of agro-technologies, rational use of land resources, improvement of approaches to the conservation and reproduction of soil fertility, soil treatment systems, fertilizers and integrated plant protection, formation of adaptive crop rotations, research of technological and biological processes in the cultivation of crops, use of GIS-technologies, remote sensing, and the use of remote sensing.

Detection of Individual Corn Crop and Canopy Delineation from Unmanned Aerial Vehicle Imagery

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level.

Impact of row ratios and nutrient management practices on yield, economics and nutrient uptake of mustard + chickpea intercropping system

A field experiment was undertaken in split plot design at ICAR- Indian Agricultural Research Institute farm, New Delhi during the rabi seasons of 2021-22 and 2022-23 determine the impact of different row ratios and nutrient management practices on yield, nutrient uptake and economics of mustard + chickpea intercropping system. Sixteen treatment combinations were taken with four different row ratios as main plots and four nutrient management practices as sub-plots replicated thrice. Though crop yields for individual crops of mustard and chickpea were found to be the highest for sole crop, but yield of intercropping system in terms of mustard equivalent yield was found to be the highest for mustard + chickpea 5:2 row ratio (2.98 t/ha in 2021 and 3.06 t/ha in 2022). Yields of both mustard and chickpea were observed to be the highest in the plots treated with combination of organic manures and microbial consortia i.e., 20 kg nitrogen through farmyard manure + leaf manure @ 4t/ha + microbial consortia (1.38 t/ha for mustard and 1.60 t/ha for chickpea). Highest gross returns (`84,560), net returns (`54,040) and benefit-cost ratio (1.77) were found in mustard + chickpea 5:2 row ratio. Analysis of nutrient uptake revealed maximum nutrient uptake in mustard + chickpea 2:5 intercropping system which led to better vegetative growth and higher yields. The above research aptly highlighted the beneficial effects of intercropping mustard with chickpea in dryland areas which helped to generate higher income for the farmers along with maintenance of soil sustainability by application of organic manures in right proportions.

Research on Segmentation Method of Maize Seedling Plant Instances Based on UAV Multispectral Remote Sensing Images.

The accurate instance segmentation of individual crop plants is crucial for achieving a high-throughput phenotypic analysis of seedlings and smart field management in agriculture. Current crop monitoring techniques employing remote sensing predominantly focus on population analysis, thereby lacking precise estimations for individual plants. This study concentrates on maize, a critical staple crop, and leverages multispectral remote sensing data sourced from unmanned aerial vehicles (UAVs). A large-scale SAM image segmentation model is employed to efficiently annotate maize plant instances, thereby constructing a dataset for maize seedling instance segmentation. The study evaluates the experimental accuracy of six instance segmentation algorithms: Mask R-CNN, Cascade Mask R-CNN, PointRend, YOLOv5, Mask Scoring R-CNN, and YOLOv8, employing various combinations of multispectral bands for a comparative analysis. The experimental findings indicate that the YOLOv8 model exhibits exceptional segmentation accuracy, notably in the NRG band, with bbox_mAP50 and segm_mAP50 accuracies reaching 95.2% and 94%, respectively, surpassing other models. Furthermore, YOLOv8 demonstrates robust performance in generalization experiments, indicating its adaptability across diverse environments and conditions. Additionally, this study simulates and analyzes the impact of different resolutions on the model's segmentation accuracy. The findings reveal that the YOLOv8 model sustains high segmentation accuracy even at reduced resolutions (1.333 cm/px), meeting the phenotypic analysis and field management criteria.

Efficiency of Factor Productivity and Effect of Individual Input of Production on Growth, Yield and Economics of Soybean [Glycine max (L.) Merrill] Production

Background: In the face of limited resources and changing climatic conditions, a major challenge for agriculturists is to ensure food security while addressing the issue of an ever-growing population. In India, the average productivity of soybean is low as compared to world average due to lack of improved agronomic practices, less importance and mostly grown under rain-fed condition. To bridge this gap, factors of production determining the per-unit production plays a crucial role in crop production. Therefore, in order to identify/evaluate the partial factor productivity and their contribution for better crop growth and individual input use efficiency study was undertaken. Methods: The field experiments were conducted during Kharif 2020, 2021 and 2022 in Randomized Block Design with seven crop management practices. To know the efficiency of individual crop management practices on growth, yield parameters, yield, economics and output efficiency in terms of partial factor productivity and agronomy efficiency of applied nutrients. Result: Three-year results concluded that, soybean grown with full package recorded significantly higher seed yield (2277 kg ha-1) over full package excluding weed management (1889 kg ha-1) and full package excluding RDF (2023 kg ha-1). The yield gap was higher with omission of weed management (388 kg ha-1) and omission of RDF (254 kg ha-1). Further, net returns and crop out-put efficiency in terms of partial factor productivity and agronomic efficiency was higher with full package over the omission of the practice. Thus it can be concluded that, for achieving higher productivity, profitability and agronomic efficiency of applied nutrients under full package comprising all crop management practices can be adopted over the years in Northern Transition Zone of Karnataka.

Impact of novel methods and research approaches in plant pathology: Are individual advances sufficient to meet the wider challenges of disease management?

AbstractAdvances continue to be made by plant pathologists on topics in plant health, environmental protection and food security. Many advances have been made for individual crops, pathogens and diseases that in many cases have led to their successful management. A wider impact of research depends on recognition of the multifaceted challenges posed by plant diseases and the need to integrate studies in a systems level approach. The adoption of high‐throughput sequencing for diagnosis and detection is widespread but impact depends upon the agricultural and ecological context combined with improved surveillance. Deployment of host resistance in the field needs to be aligned with a greater appreciation of plant genetic diversity and the complementary contribution made by tolerance of disease. Epidemiological understanding of the spatiotemporal spread of plant diseases has improved through population dynamic and genetic analyses. Research emphasis on the plant microbiome has invigorated soil microbial studies, especially for disease complexes and declines, but the challenge is to move to interventions that benefit plant health. Analysis of the impacts of climate change has been made for single‐crop disease studies, but seldom have these been placed in the context of pathogen adaptation, new crops, wild plants, vectors and soil microbes. Advances in informatic analysis illustrate not only the global impacts of plant disease introductions, but also the challenges inherent in marshalling and integrating information. Advances have been made in applying artificial intelligence technologies across many areas of plant pathology but have yet to be integrated within any coordinated research agenda.

The impacts of cover crop mixes on the penetration resistance model of an Oxisol under no-tillage

Soil penetration resistance is used as an indicator of mechanical resistance to root growth into the soil matrix. However, penetration resistance measured only under wet soil conditions may not identify the effects of cover crops on soil structure. We aimed to determine the impact of individual and mixes of cover crops on soil penetration resistance as a function of water content and bulk density in an Oxisol under no-tillage in southern Brazil. Six treatments of individual and mixed cover crops were tested: (i) black oat; (ii) ryegrass; (iii) forage radish; (iv) wheat; (v) Fibrous-rooted crop mix: with 80 % fibrous-rooted and 20 % tap-rooted plants; and (vi) Tap-rooted crop mix: 40 % fibrous-rooted and 60 % tap-rooted plants. Soil cores were collected from a depth of 0–0.10 and 0.10–0.20 m and soil penetration resistance was evaluated under four soil water matric potentials (−6, −33, −100, and −500 kPa). Soil penetration resistance (Q) data for each treatment were fitted as a function of bulk density (ρ) and water content (θ) [Q=f(ρ, θ)]. The soil penetration resistance model was a non-linear equation (i.e., a double power function, with a negative exponent for water content and a positive exponent for bulk density). Under a given combination of bulk density and water content values, soil penetration resistance was higher after single crops than crop mixes, indicating a crop-mediated soil strengthening process. The cover crop mixes increased the macroporosity, resulting in an adequate soil physical quality for successional crops such as soybean, although no differences were observed for cover crops at soil water matric potentials of −60 hPa and −330 hPa when soil penetration resistance was used as a static measurement. The modelling of the soil resistance to penetration, as a function of soil bulk density and soil water content, showed that cover crops changed soil cohesion and reduced the impact of drying on soil strength when compared to the single cropping systems, indicating that soil penetration resistance should be modelled under a broad range of water contents, instead of only under a specific soil water content, to quantify the impacts of cover crops on soil strength.

Assessing the effect of soil cultivation methods and genotypes on crop yield components, yield and soil properties in wheat (Triticum aestivum L.) and Rice (Oryza sativa L.) cropping system

BackgroundThe rice-wheat cropping system is the prevailing agricultural method in the North-Western states of India, namely in the Indo-Gangetic plains. The practice of open burning of rice residue is frequently employed for expedient land preparation, but it has significant adverse impacts on both the environment and human health. These include the emission of greenhouse gases, loss of nutrients, elevated concentrations of particulate matter (PM), and disruption of the biological cycle. This research aims to investigate the implementation of effective management strategies in the rice-wheat cropping system, namely via the use of tillage-based crop cultivation techniques, stubble retention, and integration approaches. The objective is to enhance soil health features in order to augment crop yield and improve its attributes.ResultsThe research was carried out using a split plot experimental design, consisting of three replications. The main plot consisted of four different cultivation methods, while the subplot included three genotypes of both rice and wheat. The research demonstrates the enhanced efficacy of residue application is significantly augmenting soil nutrient concentrations compared to standard tillage practices (P < 0.05). This was accomplished by an analysis of soil nutrient levels, namely nitrogen (N), phosphorus (P), potassium (K), and organic carbon (OC), at a depth of 0–15 cm. The implementation of natural farming, zero tillage, and reduced tillage practices resulted in decreases in rice grain yields of 34.0%, 16.1%, and 10.8%, respectively, as compared to conventional tillage methods. Similarly, the implementation of natural farming, zero tillage, and reduced tillage resulted in reductions in wheat grain yields of 59.4%, 10.9%, and 4.6% respectively, in comparison to conventional tillage practices.ConclusionRegarding the individual crop genotypes investigated, it was continuously observed that Him Palam Lal Dhan 1 and HPW 368 displayed considerably greater grain yields for both rice and wheat during the two-year experimental period. Furthermore, when considering different cultivation methods, conventional tillage emerged as the most effective approach for obtaining higher productivity in both rice and wheat. Additionally, Him Palam Lal Dhan 1 and HPW 368 exhibited superior performance in terms of various crucial yield components for rice (such as panicle density, grains per panicle, panicle weight, and test weight) and wheat (including effective tiller density, grains per spike, spike weight, and 1000-grain weight).

Diversified grain rotations can be highly and reliably productive in unstable climates

The crop rotation is a farm-scale management choice that dictates agronomic output, ecological impacts, and farm viability. Rotations have become less diverse recently. Re-diversifying may help agricultural systems meet the growing, dynamic demands from society and challenges from climate change. Using a long-term experiment, we tested whether diversified rotations could a) match the productivity (grain yield) of simplified rotations while b) stabilizing productivity against variable weather in the western U.S. Corn Belt. We identified rotation design choices that drive these productivity-stability dynamics. Although more productive rotations were more susceptible to weather (r = 0.63; p < 0.001), this tradeoff was surmountable (p < 0.001): the corn-oat-winter wheat-soybean (Zea mays L., Avena sativa L., Triticum aestivum L., Glycine max (L.) Merr.) rotation was a) no less productive than the highly unstable, corn-soybean rotation (p = 0.11); and b) only marginally less stable than the most stable rotation (p = 0.06). Crop selection and sequencing were critical to this outcome. High productivity was due to a) overyielding of individual crops in diversified rotations that increased with time; b) sequencing to allow higher-yielding winter crops; and c) beneficial crop legacy effects. Stability was highest in rotations that included crops belonging to more weather-niches (r2 = 0.67; p < 0.001). Surprisingly, these weather-niches groups did not correspond to traditional categories like cool- vs warm-season crops. These results suggest four principles for the design, study, and implementation of diversified grain rotations that are stable under erratic weather and are as productive as current standard practices.

Spatially Explicit Active Learning for Crop-Type Mapping from Satellite Image Time Series.

The availability of a sufficient number of annotated samples is one of the main challenges of the supervised methods used to classify crop types from remote sensing images. Creating these samples is time-consuming and costly. Active Learning (AL) offers a solution by streamlining sample annotation, resulting in more efficient training with less effort. Unfortunately, most of the developed AL methods overlook spatial information inherent in remote sensing images. We propose a novel spatially explicit AL that uses the semi-variogram to identify and discard redundant, spatially adjacent samples. It was evaluated using Random Forest (RF) and Sentinel-2 Satellite Image Time Series in two study areas from the Netherlands and Belgium. In the Netherlands, the spatially explicit AL selected 97 samples achieving an overall accuracy of 80%, compared to traditional AL selecting 169 samples with 82% overall accuracy. In Belgium, spatially explicit AL selected 223 samples and obtained 60% overall accuracy, while traditional AL selected 327 samples and obtained an overall accuracy of 63%. We concluded that the developed AL method helped RF achieve a good performance mostly for the classes consisting of individual crops with a relatively distinctive growth pattern such as sugar beets or cereals. Aggregated classes such as 'fruits and nuts' posed, however, a challenge.

Empowering Farmers and Consumers Blockchain Innovations in Agricultural Supply Chains

In recent times, numerous instances have come to light concerning farmer suicides attributed to mounting debts and meager agricultural yields. This poses a significant predicament for countries like India, which are grappling with burgeoning populations and an escalating reliance on land for residential and industrial expansion. The diminishing availability of fertile land for farming necessitates heightened agricultural output to meet the nation's demands. Despite this increasing demand, farmers continue to grapple with formidable challenges. Concurrently, consumers are also grappling with concerns over escalating commodity prices and the quality of produce available in the market. They often find themselves compelled to purchase available goods at prices dictated by sellers. Unscrupulous practices such as black market activities, hoarding, and adulteration, perpetuated by intermediaries, serve to further inflate the costs of agricultural products. Although the desire for organic products is on the rise, the absence of a robust system to trace the various stages of organic cultivation and guarantee authenticity poses a significant hurdle. Perhaps the most glaring issue in the agricultural markets is the palpable disconnect that exists between farmers and consumers. We have introduced an innovative marketplace built upon blockchain technology, designed to foster collaborative farming practices between farmers and consumers. This platform allows farmers to showcase their potential crops and projected yields on a decentralized public ledger. Consumers, in turn, gain access to this information and can assess the credibility of farmers based on their past cultivation and supply records. This pioneering approach establishes a transparent and tamper-proof digital marketplace exclusively for agricultural products. Within this framework, a consensus is established through mutual agreement between farmers and consumers. This consensus enables consumers to financially support individual crops or entire fields, in exchange for a share of the farm's yield or a percentage of the market value profit. A rating system has been implemented to enhance the credibility of both farmers and consumers, drawing from their prior interactions and experiences within the agro-market domain.