Current Crop Research Articles

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.

Latin America: A hub for agrobiotechnological innovations.

Modern biotechnology is one of the last century's major advances in human science. Particularly in the agronomical field, the landscape of crop improvement technologies has witnessed a great expansion, driven by the integration of molecular and genetic engineering methodologies into the breeding toolbox. Latin America (LATAM) serves as a pioneering region in incorporating such techniques with several countries swiftly embracing these technologies. This review aims to give a comprehensive overview of the elements that influenced agrobiotech acceptance in LATAM countries and how such cases could provide support for upcoming technologies to be considered worldwide. Nearly 50 years of biotech breakthroughs have provided humankind with an impressive portfolio of tools already integrated into several life-sciences areas. The agronomical field has greatly progressed thanks to technologies derived from Genetically Modified Organisms (GMOs) and high promises are being made to also incorporate genome -editing products. LATAM's case is a prime example of how early introduction of novelties in the crop production chain can result in improved yields, paving the way for future developments to be easily integrated into the technological ecosystem of a region. The example set by LATAM can also be useful for the present gene-editing regulatory scenario. With several countries presently on the path to approving these methods in their current crop systems, basing their next steps on the southern continent's example, could represent a safe and practical pathway towards a new agronomical revolution.

When are you measuring soil β‐glucosidase activities in cropping systems?

AbstractIn situ soil respiration is driven by annual patterns of temperature and soil moisture, but what about extracellular enzyme activities responsible for depolymerizing soil organic matter? We conducted biweekly measurements of potential soil β‐glucosidase activities during a 4‐month period from March soil thawing through July in annually cropped field plots in eastern South Dakota. Our objective was to determine the best sampling time to resolve the effects of crop rotational diversity on soil microbial activities. Potential β‐glucosidase activities were elevated immediately following soil thaw, peaked in May, and declined to their lowest value in mid‐summer. Temperature and precipitation had no value in predicting enzyme activities; however, enzyme activities were affected by crop rotational diversity and responded to current crop and previous crop. These findings are pertinent to the use of soil extracellular enzymes in soil health assessments and as indicators of microbial substrate preference with implications for soil carbon stabilization.

Enhancing crop production in the Haihe Basin while addressing challenges related to water quantity and quality

To tackle the challenge of ensuring food security while managing water availability and quality constraints in the Haihe Basin, a critical grain-producing region in China with a dense population and severe water degradation issues, we have developed a comprehensive spatial optimization (CSO) strategy. This approach integrates various crop and soil management practices, land consolidation, and cutting-edge breeding technologies. It also uses a spatial planning model based on linear programming, considering water quantity and quality constraints. Our research shows that crop yields may drop by 14 % (3780 Kt) compared to levels in 2017 due to restrictions on groundwater extraction and nitrogen (N) concentrations in leaching or runoff. However, by strategically maximizing overall crop yields through the integrated CSO strategy without changing the crop production structure, the total crop yield could potentially increase by 81 % (21,817 Kt) while using 3.0 % (86 Kha) less cultivated land than in 2017. This would help to fulfill the projected food demand in 2050. If the integrated aims to minimize the sown area after fulfilling the demand for crops in 2050, around 28 % of the sown area, or 820 Kha cropland, could be reduced compared with 2017. This reduction may save more land for natural conversation. Furthermore, total N losses, including ammonia, nitrous oxide emissions, runoff, and leaching, could be reduced by 23 %–43 % (139–252 Kt), depending on the specific optimization strategy goals. The stepwise integrated optimization strategy provides a feasible framework for the Haihe Basin, enabling the maintenance or even enhancing current crop yields while safeguarding water quality and quantity.

Early-stage soil organic carbon stabilization in conservation agriculture-based cereal systems

In South Asia, the sustainability of conventional tillage and input-intensive cereal-based cropping systems (such as rice-rice and rice-wheat) is under scrutiny due to soil organic carbon depletion, stagnant productivity, and adverse environmental impacts stemming from greenhouse gas emissions (N2O, CH4, and CO2). Against this backdrop, a long-term field experiment was initiated in 2009 at three sites in India (Karnal, Patna, and Aduthurai) and one site in Bangladesh (Gazipur) to assess four scenarios (S) reflecting current and future diversified crop rotations and conservation agriculture (CA) practices: S1 - double cereal rotation with conventional practices; S2 - double cereal plus legume rotation with partial CA; S3 - double cereal plus legume rotation with full CA; and S4 - futuristic diversified cereal-legume rotations with full CA. This study delved into the dynamics and stabilization of soil organic carbon across all scenarios and sites. Replicated soil samples were collected from depths of 0–15 cm and 15–30 cm after two crop cycles. We analyzed Walkley-Black carbon (WBC), total organic carbon (TOC), and various carbon pools with different oxidizability, determining the amount of carbon stabilized under CA-based scenarios and identifying optimal systems for carbon economy in South Asia. On average, active and passive carbon pools, TOC, and WBC stocks followed the order: S4 > S3 > S2 > S1 at all sites, except Gazipur, where the order was S3 > S4 > S2 > S1. CA practices stabilized applied carbon inputs to soil organic carbon at an annual rate of 1.7 %, with greater stabilization observed under S4 (11.7 %) > S3 (10.6 %) > S2 (7.8 %), regardless of location. Rice-rice sites exhibited a higher carbon stabilization rate (13.4 %) compared to rice-wheat sites (6.7 %). A significant proportion of stabilized carbon (63 %) was allocated to passive pools in soils under S2, S3, and S4, highlighting CA's potential to enhance carbon stability. Using a linear indexing technique, we identified that both S3 and S4 are conducive to better carbon stabilization, yield sustainability, and environmental co-benefits. Consequently, full CA systems with best management practices (S3) and best management practices with crop diversification (S4) are recommended for sustainable crop production in the major double cereal growing regions of South Asia, including India and Bangladesh.

Adapting to climate change in arid agricultural systems: An optimization model for water-energy-food nexus sustainability

Sustainable management of water, energy, and food (WEF) under climate change will be a significant challenge for arid agricultural systems. This study developed a fractional non-linear multi-objective programming (FNLMOP) model to optimize resource allocation and improve agricultural sustainability in these systems under climate change. The model was designed in the framework of the WEF nexus to simultaneously improved energy productivity (profit/energy), and water productivity (profit/water), while mitigating environmental damage (damage to groundwater resources/output) and ensuring food security in an arid watershed in Iran. The long Ashton research station weather generator (LARS-WG) and the coupled model intercomparison project 6 (CMIP6) were employed to project climate parameters for both future dry and wet conditions. The sustainability of the optimal solutions was then assessed using a hybrid criteria importance through intercriteria correlation (CRITIC)-VIKOR approach. The optimal solutions revealed a reduction in the land under cultivation and produced less water-intensive crops. The optimization model can ensure WEF security, enhancing agricultural system sustainability by optimizing crop cultivation patterns and resource allocation. Current crop choices were highly inefficient with the bigger changes being from the current crops to optimal crops. Climate change showed a substantial but lesser influence on optimal crop choice.

Politics and Orature in Beautiful Nubia’s Kurunmi is on the Way And 9ice’s Pete Pete

Music is an interesting aspect of oral literature that has witnessed different critical studies. Extant researches have explored music from the religious, ritual context, or as an aspect of ritual performance. Other studies have explored music from ideological perspective. This study is therefore justified as it attempts to examine music from the context of entertainment, oral literature and politics. This is done with the aim of exploring the political ideology behind the song and the functions and significance of the features of oral literature in the songs and its effectiveness in passing the messages to the audience. This research adopts Bronislaw Malinoslaw and Radcliffe Brown’s Functionalism as its theoretical framework. Functionalism opines that every aspect of the social life should contribute to the promotion of the society’s cultural values that must be passed from one generation to another, however, any aspect of the social life that does not promote the society’s cultural believe must be discontinued. The method of data collect were through the Beautiful Nubia and 9ice’s selected musical texts which were transcribed, translated and critically examined. This research discovered that Beautiful Nubia and 9ice uses features of oral literature to make their song more pleasing and effectively pass their political message to their audience. Beautiful Nubia engages the use legend of Aare Kurunmi to address leadership challenges facing the nation. Beautiful Nubia highlights and extols some leadership qualities Kurunmi displays such as selflessness, fearlessness, bravery and honesty that current crops of leaders can imbibe and electorate can look out for in candidates for leadership offices. This research also discover that 9ice engages proverbs in advising the electorates to be mindful of who they elect into political offices and shun electing incompetent individuals and that collecting bribes from politicians has its attendants consequences.

Optimizing Jatropha curcas bioenergy plantations in Pakistan: A geospatial suitability analysis using advanced spatial modeling

Bioenergy from energy plants, an alternative fuel, is projected to increasingly meet future worldwide energy demands as well as those of a developing nation like Pakistan. Jatropha curcas (JC) has been found to possess many desirable qualities, including high oil content seeds (27–40 %), rapid growth, ease of growing, tolerance to drought, ability to grow on poor soil and wasteland, needing less nutrient input and management, and not interfering with current food crops, insects, or pest resistance. Hence, growing JC in highly appropriate barren lands could reduce the global reliance on fossil fuels. However, an evaluation of the suitability of the land would be required for tree establishment to be effective. In this study, we integrated an Analytical Hierarchy Process (AHP) with the ArcGIS tool for assessing suitable available sites to cultivate JC across Pakistan. Using geospatial technologies, this study intended to assess and map possible areas appropriate for JC bioenergy plantations with the necessary climate, soil type, and topography factors. Pakistan was classified into 3 levels of suitability for JC plantations based on the results of a spatial analysis that integrated various data sets and varied evaluation criteria: Most Suitable, Moderately Suitable, and Less Suitable. Our results showed that out of the total area of Pakistan, around 54,392,075 hectares (57 %), 22,516,700 hectares (23 %), and 19,594,100 hectares (20 %) were identified as highly suitable, moderately suitable, and less suitable, respectively, for JC bioenergy plantations. We conclude that the methods used in this study provided a considerably reliable estimate of suitable sites for JC production in Pakistan. It can assist smallholder-based initiatives to promote JC cultivation on farmer-owned to enhance their living circumstances. Multi-criteria decision analysis (MCDA) was found to be a beneficial tool in breaking down the issues of identifying and classification of the locations for afforestation in the management of forests.

Understanding the amylose biosynthesis and regulation mechanisms in Tartary buckwheat by the endosperm transcriptome

Starch serves as a crucial energy source for both plants and humans, predominantly synthesized and stored in endosperms, tubers, rhizomes, and cotyledons. Given the significant role of amylose in determining the quality of starchy crops, optimizing its content has become a key objective in current crop breeding efforts. Tartary buckwheat, a dicotyledonous plant, notably accumulates high levels of amylose in its endosperm, surpassing common cereals like rice and maize. However, the mechanisms underlying amylose accumulation, distribution, and regulation in Tartary buckwheat remain unclear. Here, amylose content was determined across various tissues and organs of Tartary buckwheat, identifying with the endosperm as the primary site for its biosynthesis and accumulation. RNA sequencing analysis of endosperms from different developmental stages identified 35 genes potentially involved in starch biosynthesis, with 13 genes showing high endosperm-specific expression, suggesting crucial roles in starch biosynthesis. Additionally, the transcription factor FtNF-YB2, which was specifically highly expressed in the endosperm, was discovered to enhance amylose synthesis. Moreover, promoters with potential endosperm-specific activity were identified, advancing our understanding of amylose regulation. Additionally, this study also demonstrates that brassinosteroids (BR) positively influence amylose biosynthesis in Tartary buckwheat endosperm. These findings provide essential insights into the mechanisms of understanding amylose biosynthesis, accumulation and regulation in Tartary buckwheat, offering significant implications for future breeding strategies.

Reduction of uncertainties in rice yield response to elevated CO2 by experiment-model integration: A case study in East China

Accurate prediction of future rice yield needs the precise estimations of rice yield response to climate change factors, of which the most important one is the increasing carbon dioxide (CO2) concentrations. Estimates of CO2 fertilization effect (CFE) on rice, however, still had large uncertainties. Therefore, using the rice planting areas in East China as the study area, we firstly compared the rice yields and CFE predicted by four state-of-the-art crop models, and found that the CFE predicted by these models had significant differences. We then quantified the CFE on rice yield using the field-controlled experiment conducted at Danyang site at Jiangsu province. Using CFE measurements from a field experiment as benchmark, we have developed an experiment–model integration approach aiming to reduce this variation. This study thus highlights the large CFE uncertainties of current crop models and provides us with a method to reduce this uncertainty, which is beneficial for the accurate prediction of future global rice yield in the context of climate change.

Optimizing the production of dsRNA biocontrols in microbial systems using multiple transcriptional terminators.

Crop pests and pathogens annually cause over $220 billion in global crop damage, with insects consuming 5%-20%of major grain crops. Current crop pest and disease control strategies rely on insecticidal and fungicidal sprays, plant genetic resistance, transgenes, and agricultural practices. Double-stranded RNA(dsRNA) is emerging as a novel sustainable method of plant protection as an alternative to traditional chemical pesticides. Successful commercialization of dsRNA-based biocontrols requires the economical production of large quantities of dsRNA combined with suitable delivery methods to ensure RNAi efficacy against the target pest. In this study, we have optimized the design of plasmid DNA constructs to produce dsRNA biocontrols in Escherichia coli, by employing a wide range of alternative synthetic transcriptional terminators before measurement of dsRNA yield. We demonstrate that a 7.8-fold increase of dsRNA was achieved using triple synthetic transcriptional terminators within a dual T7 dsRNA production system compared to the absence of transcriptional terminators. Moreover, our data demonstratethat batch fermentation production dsRNA using multiple transcriptional terminators is scalable and generates significantly higher yields of dsRNA generated in the absence of transcriptional terminators at both small-scale batch culture and large-scale fermentation. In addition, we show that application of these dsRNA biocontrols expressed in E. coli cells results in increased insect mortality. Finally, novel mass spectrometry analysis was performed to determine the precise sites of transcriptional termination at the different transcriptional terminators providing important further mechanistic insight.

Kansas agriculture in 2050: a pathway for climate-resilient crop production

IntroductionAdvances in farming technologies and practices have helped farmers to increase crop yields, but continued production increases – while needed to feed a growing global population – are being hindered by climate change and other environmental challenges. Kansas, a globally important region for wheat production, is already experiencing threats to staple crop production. This study explores one possible alternative future for Kansas crop production in which farmers could, by crop switching, continue to grow nutrient-rich crops while responding to hotter temperatures and increasing water demand.MethodsWe used a combination of climate and crop modeling with simple water budgets to identify optimal crop mixes under anticipated climatic and water constraints. Interviews with Kansas farmers helped identify feasible crop switching options: sorghum instead of corn (maize), winter rye and winter oats instead of winter wheat, and millet instead of soybeans.ResultsOur analysis suggests that a sizeable proportion of current Kansas cropland would need to shift to these alternative crops by 2050 to meet anticipated water constraints and produce equivalent nutritional value under projected climate conditions. Alternative crops could increase from 16% of Kansas' crop area in 2021 to 43% of the area in 2050, resulting in a reduction in 2050 crop water demand of 12% relative to that of the current crop mix. This crop water demand reduction would be concentrated in parts of the state that will experience the greatest change in water needs between today and mid-century due to changing climate conditions.DiscussionOur analysis shows that, by changing (diversifying) the mix of crops grown, it is biophysically possible for crop production in Kansas to be both sustainable and resilient under future climate conditions. However, achieving a more climate-resilient crop mix on the ground, in Kansas and elsewhere, will require major shifts in the broader agricultural system. Food companies, agricultural lenders, and policymakers can play a key role in enabling farmers to adapt cropping systems in the face of climate and environmental challenges.

Effects of retained node numbers on berry maturity and yield components of cane-pruned Sauvignon blanc

Cane pruning is used in most New Zealand Sauvignon blanc vineyards to manage yield, vine balance (relationship between vegetative growth and fruit growth) and fruit primary and secondary metabolites. The source–sink ratio (TLA/FM—total leaf area to fruit mass or ELA/FM—exposed leaf area to fruit mass), the fruit mass to pruning mass (FM/PM), the fruit mass to cane mass (FM/CM) and fruit composition provide an assessment of the vine performance and balance. The interpretation of these metrics (i.e., TLA/FM, ELA/FM, FM/PM, FM/CM) requires their comparison with known optimal ranges specific to cultivars, locations and growing conditions. More often, such context- and cultivar-specific optimal ranges do not exist, thus warranting research to investigate them. To understand the influence of retained node numbers on the vegetative and fruit development of Sauvignon blanc, grapevines were pruned across three vineyard sites (two in Marlborough—Site 1 and 2, and one in Waipara—Site 3) over two growing seasons, retaining 10, 20, 30, 40 and 50 nodes on one to four canes (each cane carrying ten nodes, with 50-node vines carrying on average 12.5 nodes on each of the four canes). The accumulation of soluble solids (TSS) generally increased at lower node numbers and vine yields, reflecting an increase in ELA/FM, with 50-node vines having the least TSS concentration at harvest. The average berry mass, titrable acidity (TA) and pH were unaffected by node numbers over the two seasons. A low source–sink ratio induced by high node numbers not only reduced the vine capacity to ripen the current crop but also reduced the following season’s bunch number per shoot (from 1.8 to 1.6 bunches per shoot; p < 0.05), average bunch mass (from 82.0 ± 6 g to 67.7 ± 3 g; p < 0.01) and bunch mass per shoot (from 153.5 ± 15 g to 106.7 ± 9 g; p < 0.05). When compared to 50-node vines, 10-node vines had a two-fold increase in the average cane mass (from 30.1 ± 3.9 g to 69.2 ± 8.7 g; p < 0.001) and average old cane mass (from 82.4 ± 6.9 g to 163.8 ± 21 g; p < 0.001). The ELA/FM and TLA/FM required for optimal TSS accumulation were 0.75 m2 kg-1 and 2.0 m2 kg-1, respectively, across all sites. A source–sink ratio above these values resulted in high average cane mass and average old cane mass (an indication of excess vigour), while lower values indicated reduced vigour and slowed TSS accumulation. This research provides useful optimal ranges to compare and interpret vine balance metrics measured at those sites.

First report of Ug99 Wheat Stem Rust caused by Puccinia graminis f. sp. tritici in South Asia.

Since the emergence of Ug99 wheat stem rust in Uganda in 1998 (Pretorius et al. 2000), the threat of movement into South Asia has been a concern due to long-distance dispersal capacity of airborne spores (Brown and Hovmøller 2002; Singh et al. 2008; Meyer et al. 2017). Increased preparedness by comprehensive rust surveillance efforts and development and deployment of resistant cultivars in advance of an incursion into South Asia has been one of the success stories of the Borlaug Global Rust Initiative (Sharma et al. 2013). In November 2023, an off-season rust survey was conducted in Marpha, Gandaki and Bagmati provinces in Nepal. Rust was only observed at two sites, Dangdunge of Dolakha district and Mude of Sindhupalchok district, where spring wheat was grown as fodder crop outside the main cropping season. Rust infected wheat leaves (10-15 leaves per site) were air dried and sealed in envelopes that were shipped under permit to the Global Rust Reference Center, Denmark. Bulk samples of stem rust, Puccinia graminis f.sp. tritici (Pgt), were recovered from both envelopes, and single pustule isolates were raised and multiplied on Morocco and McNair. Meanwhile, specimens of dry leaves were subjected to SSR genotyping according to standard procedures (Patpour et al. 2022). One distinct multi-locus Pgt genotype was observed, identical to and representing 99% of Ug99 isolates within Clade I collected in East Africa between 2012-2022. A Pgt single pustule isolate from each of the sampling sites were inoculated onto 20 internationally agreed stem rust differential lines using standard procedures, and 14 supplementary lines providing additional resolution of pathogen virulence (Patpour et al. 2022). The pathotyping was repeated in two independent experiments, which resulted in the infection type pattern of Pgt race TTKTT (Supplementary Table 1). Additional independent SSR genotype assays of recovered isolates confirmed the prevalent genotype of Clade I (Patpour et al. 2022; Szabo et al. 2022). This first detection of Ug99 race TTKTT in South Asia emphasizes the need for continued coordinated international surveillance efforts and utilization of diverse sources of resistance to control stem rust in wheat. New surveillance efforts in Nepal during February-March 2024 did not reveal additional cases of wheat stem rust. However, more detailed and sustained rust surveillance efforts, assessment of the vulnerability of current wheat crops to Ug99 and other races of stem-, stripe/yellow- and leaf rust, as well as intensified breeding for rust resistance throughout the region is strongly recommended to meet current and future plant health risks.

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.

Insufficient pollinator visitation often limits yield in crop systems worldwide.

Declining pollinator populations could threaten global food production, especially if current crop yields are limited by insufficient pollinator visitation to flowers, in a phenomenon referred to as 'pollinator limitation'. Here, we assess the global prevalence of pollinator limitation, explore the risk factors, such as crop type or geographic region, that predict where pollinator limitation is more likely and ask by how much increases in pollinator visitation could improve crop yields. We address these questions using 198,360 plant-pollinator interactions and 2,083 yield measurements from 32 crop species grown in 120 study systems. We find that 28-61% of global crop systems are pollinator limited and that this limitation most frequently occurs in blueberry, coffee and apple crops. For a few datasets, we note that the probability of pollinator limitation decreases with greater forest land cover surrounding a crop field at 1 km, although average effect sizes are small. Finally, we estimate that for those crops we identify as pollinator limited, increasing pollinator visitation at all farms to existing levels observed in the 90th percentile of each study system would close 63% of yield gaps between high- and low-yielding fields. Our findings show variations in sensitivity to pollinator limitation across diverse crop systems and indicate that realistic increases in pollinator visitation could mitigate crop yield shortfalls attributable to pollinator limitation.

Classifying methods to quantify crop nitrogen input requirements

In this short communication, we propose a framework that classifies methods for estimating crop nitrogen (N) input requirements along two dimensions: short-term versus long-term and current versus optimal crop management practices. The four resulting quadrants provide different perspectives on modelling methods, each distinctive in handling soil N supply, mineral versus organic N sources, management and yield levels. For each quadrant, study examples and data requirements are provided. We emphasise the importance of accurate and purpose-oriented selection of crop N requirement modelling methods and acknowledge the complexity and uncertainties inherent in N modelling, often due to a lack of data availability. The choice of methods to estimate N inputs strongly impacts model outcomes and conclusions, affecting agricultural practices, advisories, and policy strategies. Therefore, a decision tree is presented to support choices in assessing N input requirements so that models can help identify under and over-fertilisation hotspots, maintain soil fertility, and optimise crop yields, contributing to environmental protection and food security goals.

A multi-scale semantic feature fusion method for remote sensing crop classification

The efficient and accurate acquisition of crop spatial planting structure information is of great significance to ensure national food security. Current crop classification studies have still not achieved optimal results in accurate crop classification identification due to the complex heterogeneous details of clear images and insufficient analysis and utilization of information. This study takes the Yuncheng-Linfen Basin in Shanxi, China as the study area and uses the ReliefF-RFE feature selection algorithm to achieve data dimensionality reduction. Contextual semantic feature aggregation, spatial channel attention mechanism, and hierarchical refinement strategy are used to fuse the multi-scale feature information in the extracted remote sensing images from the top down and bottom up. This study shows that 1) the ReliefF-RFE algorithm can effectively reduce the dimensionality of 224-dimensional multisource features and generate a 31-dimensional optimal feature space subset with strong interpretation to improve the computational efficiency of the model; 2) the multi-scale feature fusion model constructed in this study is better than UNet, ResNet, DeepLabv3+, HRNet and Swin Transformer in classification recognition of winter wheat and corn, with an overall accuracy of 95.76% and 93.44%; 3) A multiple feature fusion strategy designed through ablation experiments was evaluated to evaluate the reasonableness of each module and validate the irreplaceability and superiority of the model for improving crop accuracy in complex and heterogeneous terrain; 4) the multi-scale feature fusion model converts deep abstract features into local detail information by cross-scale contextual aggregation of deep and shallow multi-scale features, it obtains good extraction effect on fragmented small plots, and the reduction of local fine details of features is significantly better than other models. The multi-scale feature fusion classification model constructed in this study fully considers the interpretability of the input features and fuses the semantic features at different scales to eliminate the “semantic gap”, which shows that the model has strong stability and robustness and provides a reference for fast and accurate mapping of Sentinel-2 images of crops in large areas.

Changes in climate attributes and harvest area structures jointly determined spatial-temporal variations in water footprint of maize in the Beijing-Tianjin-Hebei region

Irrigation contributes significantly to boosting crop yield and ensuring food security. However, in the Beijing-Tianjin-Hebei (BTH) region, unsustainable irrigation practices have led to serious outcomes on freshwater resources. Balancing irrigation with crop productivity in this region, currently facing complex challenge, requires a comprehensive understanding of its spatial pattern and thus to seeking for potential optimization of current crop structures. In this study, we employed the concept of water footprint (WFP) to assess the spatial-temporal patterns of water footprint for maize in BTH region at the county level for the years 2005, 2010, 2015, and 2020, untangled the relative impacts on WFP from climate attributes and harvest area structures. Our results showed significant regional heterogeneities in both blue water requirement and green water requirement, ranging from 64.6 mm to 290.7 mm. Yearly anomalies of climate attributes and maize harvest jointly influenced water footprints, with the highest value of 1.06 × 1011 m3 occurring in the year 2015. The green water footprints, linked to precipitation, dominated the total water footprint compared to the blue water footprint associated with irrigation. Additionally, we observed an increasing influence of maize harvest area on the temporal changes in water footprints, with these changes becoming more concentrated in the east-central region over time. Our findings underscore the respective contributions of annual climate attribute changes and harvest area variations at the county level, highlighting regions where urgent interventions are required to enhance the sustainability of water usage for agriculture.

Application of Panchagavya, a Cow-based Liquid Formulation, as a Lever for Sustainable and Enhanced Vegetable Crop Production: A Review

Panchagavya, a fermented liquid bio-formulation derived from cow-based products such as dung, urine, milk, curd and ghee, has recently gained importance in agricultural practices due to its reported effectiveness in enhancing vegetable crop production. Its formulation involves a fermentation process that harnesses the diverse microbial communities present in cow-derived substances, resulting in a potent nutrient-rich solution that is believed to possess plant growth-promoting properties. One of the primary mechanisms underlying its efficacy lies in the diverse array of microorganisms present in the formulation. These microorganisms, including beneficial bacteria, fungi and actinomycetes, play crucial roles in nutrient cycling, disease suppression and soil conditioning. Additionally, Panchagavya contains plant growth-promoting substances such as hormones, enzymes, vitamins and amino acids, which contribute to enhanced nutrient uptake, root development and overall plant vigour. Furthermore, application of Panchagavya has been shown to improve soil health by enhancing microbial activity, increasing organic matter content and improving soil properties. These soil improvements not only benefit the current crop but also have long-term positive implications for soil fertility and sustainability. This multifaceted approach to crop management offers a holistic solution to enhance crop productivity while reducing reliance on synthetic inputs. This review aims to comprehensively analyse the literature on the effects of Panchagavya on vegetable crops, highlighting its mechanisms of action and potential benefits for increased vegetable crop production.