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Cover crops (CCs) offer ecosystem benefits, yet their impact on subsequent crop yields varies with climate, soil, and management practices. Using the Decision Support System for Agrotechnology Transfer (DSSAT) at the University of Nebraska-Lincoln's Eastern Nebraska Research, Education, and Extension Center (ENREEC), we identified optimal cereal rye management strategies focusing on planting, termination, and the intervals between CC termination and corn planting. Results showed minimal impact of CC management variations on corn yield, underscoring corn's resilience to management changes. Delayed planting notably decreased CC biomass, nitrogen uptake, and biomass nitrogen content on average by 8.8%, 11%, and 9.2% for every five-day delay from 25 September. Every 5-day increase in the interval between CC termination and corn planting reduced biomass by 19.3%. Conversely, each 5-day delay in CC termination from 10 September to 10 October increased biomass by 30%, enhancing SOC accumulation. SOC changes over the 30-year simulation ranged from 5.8% to 7.7%, peaking with late May terminations. The earliest termination showed the highest nitrogen content in biomass (3.4%), with the lowest (0.69%) in mid-May. Our results demonstrate that strategic CC management supports soil health without negatively impacting corn yield in Eastern Nebraska, providing valuable insights for farmers and practitioners aiming to implement sustainable CC practices while preserving crop productivity. [ABSTRACT FROM AUTHOR]

This research focuses on the seasonal patterns of the ecophysiological recovery of four olive cultivars (Arbequina, Coratina, Frantoio, and Urano), both micropropagated and self-rooted, grown in a mature, irrigated, super high-density (SHD) orchard under Mediterranean conditions (Southern of Italy). The aim was to observe the impact of the micropropagation method on the ecophysiological responses. Ecophysiological parameters, including leaf water potential (LWP), stomatal conductance (gs), net photosynthetic rate (Pn), and transpiration rate (E) were assessed. Self-rooted trees consistently exhibited superior gs, E, and Pn recovery compared to the micropropagated ones. 'Arbequina' maintained elevated levels of Pn under water-deficit conditions. 'Coratina' exhibited increases in gs and E after irrigation. 'Frantoio' demonstrated recovery capabilities, with lower LWP and higher Pn under stress. 'Urano' micropropagated trees achieved higher gs values in mid-summer, while self-rooted trees sustained higher Pn later in the season. This field research highlighted the important role of the propagation method in optimizing the physiological performance of olive cultivars in SHD orchards. Furthermore, it highlighted the necessity of long-term studies on the effects of propagation methods and their interactions with other farming practices. [ABSTRACT FROM AUTHOR]

Global wheat production has remained stable in the last 20 years, benefiting from increased grain yields despite decline in harvested wheat area. Here, we conducted a comprehensive review of ca. 300 peer-reviewed studies worldwide to outline benefits of adding wheat to simple crop rotations (i.e., one to three rotational crops). We highlight the wheat's versatility for tactical in-season crop management (e.g., flexible sowing dates, crop type [winter vs spring], and nitrogen fertility) and strategic cropping system management (e.g., grazing and double-cropping) and provide evidence supporting the positive impact of wheat on the grain yield and yield stability of other rotational crops. The inclusion of wheat in simple cropping systems enhances agroecosystem diversity and improves resilience to biotic and abiotic stresses. The high carbon-to-nitrogen ratio (C:N) residue of wheat offers benefits and drawbacks on soil quality attributes, weed control, and climate change mitigation potential. The introduction of wheat to simple crop rotations can (i) interrupt pest population cycles by serving as a break crop; (ii) decrease N application requirements, thus reducing N losses, greenhouse gas emissions, soil acidification, and production costs; (iii) improve soil health and carbon sequestration; (iv) increase resource use-efficiency of the cropping system; (v) foment fauna population; and (vi) decrease variability in economic returns. This review highlights that wheat offers unique opportunities to increase diversification and foster more sustainable and resilient agroecosystems that will feed a growing global population while acting as a net carbon sink, helping to mitigate drivers of climate change. [ABSTRACT FROM AUTHOR]

Babaco is a hybrid cultivar native to the Andean region of Ecuador and Colombia, commercially attractive for its fruit. Babaco production in Ecuador faces losses from plant pathogens like babaco mosaic virus (BabMV), an RNA virus that causes chlorosis, leaf mottling, and deformation. Phylogenetic studies link BabMV to papaya mosaic virus (PapMV), alternanthera mosaic virus , and senna mosaic virus. To address this threat, we developed novel species-specific primers to detect BabMV targeting a 165 bp region of the coat protein (CP). Genus-specific primers were designed to validate the species-specific primers and attest their ability to discriminate between BabMV and its closest relatives. These primers targeted a 175 bp fragment of the CP region. The most effective sets of primers were chosen for reverse transcription polymerase chain reaction (RT-PCR) and SYBR® Green-based quantitative reverse transcription polymerase chain reaction (RT-qPCR) in symptomatic and asymptomatic babaco plants. Among 28 plants tested, 25 were positive and 3 were negative for BabMV using species-specific and genus-specific primers in RT-PCR and RT-qPCR, while the PapMV positive control was detected with the genus-specific primers and was negative for the species-specific primers. These primers represent a valuable molecular tool for detecting BabMV, potentially enhancing crop management. [ABSTRACT FROM AUTHOR]

Weeds are unwanted and undesirable plants that interfere with cultivated crops for utilization of natural resources such as nutrients, water, and light, space, causing harbor of insect pests and disease adverse allelopathic effects and increasing cost of production indirectly, reducing crop yield. India is an agrarian country, where cropping systems form the backbone of Indian agriculture. Every cropping system has its associated weeds, and to manage these weeds requires a specific crop management approach. The main rice-based cropping systems in India, such as rice-wheat, rice-rice, rice-pulse, rice-maize, and rice-oilseed, have been pivotal in ensuring food security. However, these systems face significant challenges due to weed infestations, leading to yield losses ranging from 13% to 97%. Weeds like grass, sedge and broad-leaf weed varieties pose a substantial threat to these crops, particularly in direct-seeded conditions. To manage weeds effectively, various approaches are employed, including crop rotation, tillage, herbicide application, and intercropping. For instance, in rice-wheat systems, zero-tillage practices, crop residue mulch, and early sowing help to reduce weed emergence. In rice-pulse systems, pre-emergence herbicides, hand weeding, and intercropping prove effective. Maize-based systems benefit from tillage, mulching, and planting at closer spacing. Weed management is critical to sustaining rice-based cropping systems and ensuring food security in India. Tailored strategies that consider crop-specific challenges are essential to mitigate yield losses and maintain agricultural productivity. [ABSTRACT FROM AUTHOR]

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown‐eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown‐eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown‐eye spot, identifying one with potential for advanced decision‐making. The top‐performing models were then employed in the next stage to forecast and spatially project the severity of brown‐eye spot across 2681 key Brazilian coffee‐producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman–Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water‐balance calculation. Six ML models – K‐nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) – were employed, considering disease latency to time define input variables. Results: These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high‐yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low‐yielding scenarios. The incidence of brown‐eye spot varied noticeably between high‐ and low‐yield conditions, with significant regional differences observed. The accuracy of predicting brown‐eye spot severity in coffee plantations depended on the biennial production cycle. High‐yielding trees showed superior results with the XGBoost model (R2 = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low‐yielding conditions (precision 0.76, RMSE = 12.82). Conclusion: The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown‐eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The consumption of cassava, a vital staple food for more than 1 billion people worldwide, holds particular significance in sub‐Saharan Africa (SSA). Expansion in cassava production in SSA is driven by increasing market demand, local consumption, and adaptability to diverse environments. However, cyanide concentration in cassava tubers and products across SSA ranges from 9 to 1148 ppm – most exceed the World Health Organisation's recommended safe level of 10 ppm. Such variation and high cyanide concentrations in cassava products are expected to be exacerbated by climate‐induced increases in the frequency, intensity and occurrence of drought, heat waves and biotic stresses, further jeopardising regional food security. Thus, it is essential to examine cassava production and cyanide toxicity under climate change and their implications for food security in SSA. In this review, we look at the drivers of cassava production and spatial variation in cyanide concentrations across SSA, impacts of climate variability and biotic stresses on cassava cyanide concentrations in SSA, and crop management practices for reducing cyanogenic glucosides in cassava tubers. We surmise that urgent actions are required to adopt improved cassava varieties and management strategies that reduce cassava cyanide toxicity amid climate‐induced challenges in SSA. [ABSTRACT FROM AUTHOR]

Agriculture plays a crucial role in the economy, and there is an increasing global emphasis on automating agricultural processes. With the tremendous increase in population, the demand for food and employment has also increased significantly. Agricultural methods traditionally used to meet these requirements are no longer adequate, requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers. Integration of technologies such as the Internet of Things, wireless communication, machine learning, artificial intelligence (AI), and deep learning shows promise in addressing these challenges. However, there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency. To address this gap, a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed, Scopus, Google Scholar, Web of Science, and Science Direct for relevant articles. Out of 327 initially identified articles, 180 were deemed pertinent, focusing primarily on AI's potential in enhancing yield through better management of nutrients, water, and weeds. Taking into account research findings worldwide, we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and determine the best time for irrigation or herbicide application. The present status of AI-driven automation in agriculture holds significant promise for optimizing agricultural input utilization and reducing resource waste, particularly in the context of three pillars of crop management, i.e. nutrient, irrigation, and weed management. [ABSTRACT FROM AUTHOR]

African green monkeys (Chlorocebus sabaeus) are an established introduced species in the Federation of St. Kitts and Nevis, and the source of human-wildlife conflict. The Government of St. Kitts and Nevis has responsibility to manage problems caused by the monkeys, which include real and perceived social, economic, and ecological impacts, the most notable being agricultural damage to crops. These problems are widespread across the island and affect many community members in diverse ways. This study asked if the governmental policy environment enabled or impeded intersectoral management of monkeys. No formal policy addressed all harms or benefits the monkeys engender across multiple places and sectors, nor enabled an all-of-government approach. Government actions were guided by unwritten policies operating under the authorities of agriculture, conservation, or public health legislation and international agreements. No policies addressed harms while concurrently preserving the benefits the monkeys afford, such as their promotion and use in tourism. Private sector policies addressed animal welfare and exportation of animals used in biomedical research. Cross-sectoral governance was achieved through informal mechanisms. No metrics were found to evaluate effectiveness, acceptability or feasibility of government efforts. The Government of St. Kitts and Nevis has the intentions and components that could support an integrated One Health, healthy public policy approach to its monkey problems, but all actions to concurrently protect the health of monkeys, people, incomes and ecosystems were achieved through informal means which present certain implementation and assessment challenges. Written policies could increase transparency of decision making, aid in policy evaluation and ensure sustainability of action through variation in personnel and other organizational change. [ABSTRACT FROM AUTHOR]

Introduction: One of the non-biological stresses for crop plants is drought and lack of water, which is the most important factor limiting the growth of crop plants in arid and semi-arid areas, including Iran. Drought impairs normal growth, disturbs water relations, and reduces water use efficiency in plants. Plants, however, have a variety of physiological and biochemical responses at cellular and whole organism levels, making it a more complex phenomenon. The rate of photosynthesis is reduced mainly by stomatal closure, membrane damage, and disturbed activity of various enzymes, especially those involved in ATP synthesis. Plants display a range of mechanisms to withstand drought, such as reduced water loss by increased diffusive resistance, increased water uptake with prolific and deep root systems, and smaller and succulent leaves to reduce transpirational loss. Therefore, the effects of drought stress and seed nutritional priming on plants can play an important role in managing different irrigation regimes to deal with adverse environmental conditions and improve crop yield management. Materials and methods: Quinoa is one of the crops that has received special attention in recent years due to its high nutritional value and resistance to dehydration. For this purpose, an experiment was conducted in the form of a factorial split plot, with three rounds of irrigation of 7, 10 and 14 days as the main factor and seed priming at two levels without prime as a control and Iron sulfate + zinc sulfate combination as the second factor was applied on three genotypes (Q12, Q29 and Giza1) of quinoa as secondary factors, in the crop year of 2021-2022 in the regions of Neishabour and Kashmar. Results and discussion: Moisture stress decreased the relative water content and percentage of membrane stability. In Kashmir region, the highest relative water content was observed in cultivars Q12 and Giza under the treatment of irrigation once every 14 days. While in the Neishabur region, the highest value of the mentioned variable was obtained in the treatment of irrigation once every 7 days and cultivar Q29. Slicing analysis of the interaction effect of cultivar and irrigation treatments and the interaction effect of irrigation and priming in Kashmir region showed that the 7 and 10 day irrigation treatment was significant on cell membrane stability. Comparison of the average interaction effect of the three tested factors in Neishabur area showed that the variety Q12 (regardless of seed priming) along with applying 14 days of irrigation caused a significant increase in membrane stability in Neishabur. Although the lowest level of membrane stability was obtained in the priming treatment of Q12 and Giza cultivars with the application of 7 days of irrigation once. It was observed that the priming treatment of Q29 and Giza cultivars with irrigation every 10 and 7 days, respectively, showed the highest amount of total chlorophyll in the Kashmir region, while in the Neishabur region, the highest amount of total chlorophyll was obtained in non-priming of Giza cultivar with the application of 10 Irrigation once a day. The results of the experiment showed that seed priming of Q12 and Q29 cultivars under irrigation once every 7 days caused a significant increase in seed yield in Kashmir region. In Neishabur region, the highest amount of seed yield was obtained in the non-priming cultivar Giza under irrigation once in 10 days. Conclusion In general, seed priming with zinc sulfate and iron sulfate can improve the physiological characteristics of quinoa under water stress. Although in regarding the relative water content, the Q29 variety had a dominant effect on the priming treatment in Neishabour region. According to the results of the Q29 variety in the Kashmar region and the Giza variety In the Neishabour region, it will help plant growth by maintaining the chlorophyll content of the leaves under low irrigation conditions. [ABSTRACT FROM AUTHOR]

Under global change and climate variations, determining the impacts of climate change and human activities on cropland net primary productivity (NPP) in Bangladesh, India and Myanmar (BIM) is of great significance for identifying yield-limiting factors, making adaptive agricultural management plans, and improving yields. Based on the GLOPEM-CEVSA model, through an integration of remote sensing data and LAI simulation, we investigated the impacts and spatiotemporal changes of water and human activities on BIM from 1982 to 2015. Three types of cropland NPPs were considered: actual NPP (NPPA), NPP affected by temperature and water (NPPWT), and NPP only affected by temperature (NPPT). Our analysis revealed that the water factor plays a predominant role in determining the NPP level in the BIM. Temperature variability was found to be conducive to NPPT, exhibiting an increasing trend of 10.66 g C m–2 yr–1. However, this trend was partially offset by precipitation variability, resulting in a net increase of 0.96 g C m–2 yr–1. In comparing temperature-driven NPP to temperature and water-driven NPP, water stress caused NPPT to decrease by 65.46% compared to NPPWT for the entire region. Cropland NPP in northwestern India and the central Deccan Plateau were significantly affected by water stress. Moreover, the influence of water on NPP in the BIM exhibited a substantial upward trend from 1982 to 2015, with Myanmar experiencing the most significant increase. The gap between NPPWT and NPPA in BIM demonstrated a notable decreasing trend during the same period, underscoring the positive impact of human activities on NPP. Inferences drawn from our findings suggest that with the implementation of rational and efficient crop management practices, there is a 36.80% potential improvement in NPPA compared to NPPWT in the BIM region, with India and Myanmar showing potential increases of 39.20% and 38.29%, respectively. These insights provide guidance for practical measures aimed at water resource management to enhance cropland productivity in the BIM, and they present a methodology for quantifying the effects of climatic changes and human activities at a regional scale. [ABSTRACT FROM AUTHOR]

Soil is a heterogeneous medium that exhibits considerable variability in both spatial and temporal dimensions. Proper management of field variability using variable-rate fertilization (VRF) techniques is essential to maximize crop input–output ratios and resource utilization. Implementing VRF technology on a localized scale is recommended to increase crop yield, decrease input costs, and reduce the negative impact on the surrounding environment. This study assessed the agronomic and environmental viability of implementing VRF during the cultivation of summer maize using an on-the-go detector of soil total nitrogen (STN) to detect STN content in the test fields. A spatial delineation approach was then applied to divide the experimental field into multiple management zones. The amount of fertilizer applied in each zone was determined based on the sensor-detected STN. The analysis of the final yield and economic benefits indicates that plots that adopted VRF treatments attained an average summer maize grain yield of 7275 kg ha−1, outperforming plots that employed uniform-rate fertilization (URF) treatments, which yielded 6713 kg ha−1. Through one-way ANOVA, the yield p values of the two fertilization methods were 6.406 × 10−15, 5.202 × 10−15, 2.497 × 10−15, and 3.199 × 10−15, respectively, indicating that the yield differences between the two fertilization methods were noticeable. This led to an average yield increase of 8.37% ha−1 and a gross profit margin of USD 153 ha−1. In plots in which VRF techniques are utilized, the average nitrogen (N) fertilizer application rate is 627 kg ha−1. In contrast, in plots employing URF methods, the N fertilizer application rate is 750 kg ha−1. The use of N fertilizer was reduced by 16.4%. As a result, there is a reduction in production costs of USD 37.5 ha−1, achieving increased yield while decreasing the amount of applied fertilizer. Moreover, in plots where the VRF method was applied, STN was balanced despite the reduced N application. This observation can be deduced from the variance in summer maize grain yield through various fertilization treatments in a comparative experiment. Future research endeavors should prioritize the resolution of particular constraints by incorporating supplementary soil data, such as phosphorus, potassium, organic matter, and other pertinent variables, to advance and optimize fertilization methodologies. [ABSTRACT FROM AUTHOR]

Planting date and seeding rate affect the microclimate within a crop field. They can influence lepidopteran insect infestation and fungal infections and are an important part of crop management strategy. Altering planting date and seeding rate can also influence yield and quality of silage corn (Zea mays L.). Field research was conducted to identify optimum planting time and seeding rate to minimize insect feeding, ear rot infections, and mycotoxin accumulation in silage corn without compromising yield and quality. Replicated field trials were conducted across multiple site‐years in Michigan with three planting dates (early: April 25–May 10; mid: May 11–25; and late: May 26–June 10) and/or four seeding rates (ranging from 69,160 to 113,620 seeds ha−1, in increments of 14,800 seeds ha−1). Mid‐planting yielded 12%–15% less forage than early and late plantings, partly due to greater insect feeding injury and fungal infections observed in the former. Neutral detergent fiber digestibility, starch, and crude protein concentration were greatest for early planting. Greater predicted milk per hectare and milk per megagram for early planting also indicated superior silage quality. Increasing seeding rate increased insect feeding and ear rot injury only when severity was >5% and >15%, respectively. The impact of increasing seeding rate on dry forage yield was specific to each site‐year. Overall, results showed that early planting of silage corn helps to escape insect and disease pressure and provides better yield and quality, while seeding rate response is variable and dependent on the field environment. Core Ideas: Escaping insect feeding and ear rots by altering the planting date is critical to protect yield and forage quality.Delaying silage corn planting reduced forage neutral detergent fiber digestibility.Insect feeding severity and ear rot increased at high seeding rates (≥98,800 seeds ha−1).Higher seeding rates led to reduction in neutral detergent fiber digestibility and predicted milk yield. [ABSTRACT FROM AUTHOR]

Crop models are valuable tools for simulating and assessing genotype‐by‐environment interactions. In most studies, these models are parameterized based on crop data from a few sites and years, which often limits their applicability to a broader geographic context. Therefore, we utilize countrywide multi‐environment variety trial data in this study to implement a genotype‐specific model parameterization for winter rye (Secale cereale L.) in Germany. We use the Crop and Environment REsource Synthesis (CERES) model originally used for wheat available in the decision support system for agrotechnology transfer (DSSAT) framework and adapt and evaluate it for rye. Calibration and evaluation involved a comprehensive agronomic trial datasets for the rye cultivar Palazzo, encompassing 194 site‐years of experiments covering various cereal production regions in Germany. The parameterization followed a structured approach, encompassing phenology, growth, and yield‐specific coefficients. The parameterized CSM‐CERES‐Rye (where CSM is cropping system model) demonstrated reasonable accuracy in simulating critical crop parameters, including aboveground biomass, leaf area index, tiller, grain number, unit seed weight, and grain yield. The model is available for diverse model‐based assessments of rye cultivation, including evaluating crop management, analyzing crop rotations, and assessing rye's suitability across varied environments, making it valuable for sustainable agriculture and decision‐making. Core Ideas: Adapting DSSAT‐CSM‐CERES‐Wheat (where DSSAT is decision support system for agrotechnology transfer, CSM is cropping system model, and CERES is crop and environment resource synthesis) for cereal rye using comprehensive data from German plant variety trials for the first time.A structured sequential method was employed for model parameterization.The model enables essential analyses, including crop management, rotations, and suitability assessments. [ABSTRACT FROM AUTHOR]

The variability of crop load in Conference pears significantly influences fruit quality and postharvest losses. This study aimed to investigate and implement a novel technique for managing crop load in Conference pear orchards, with a focus on contributing to sustainable orchard management practices. High-russet pear cv. Conference fruit was harvested from trees categorized into two groups based on yield efficiency. A two-way ANOVA was used to test the effects of crop load level and year on yield properties and fruit quality parameters. Results demonstrated that low crop loads exerted a positive influence on fruit quality, resulting in increased fresh-market yield, enhanced fruit-mass parameters, and improved firmness and acidity. Additionally, fruit from low crop load levels exhibited a heightened level of characteristic skin russeting, a desirable attribute that enhances product appeal, compared to fruit from high crop load levels. Thus, crop load management in Conference pear orchards emerged as an effective preharvest organic strategy for mitigating postharvest quality losses during storage, thereby promoting overall orchard sustainability. [ABSTRACT FROM AUTHOR]

Mineral nutrition is essential for plant growth and the interaction of plants with biotic and abiotic stresses. Mango twig-tip dieback (MTTD) is a new type of mango decline, but its impact on trees' mineral nutrition is unknown. This study was conducted to determine the effect of MTTD infection on the nutrient status, balance, and resorption efficiency (RE) of mangoes. Leaf nutrient concentrations and deviation from the optimum percentage (DOP) indices of 'Kensington Pride' (KP) mango trees with low (LD) and high (HD) levels of MTTD infections were analyzed to compare the foliar nutrition status and nutrient balance between the LD and HD trees. Moreover, the nutrient resorption efficiency of MTTD-infected dried leaves (RED) was compared with the resorption efficiency of healthy (RES) leaves of KP mangoes. The concentrations of total Ca, Mg, Cu, Fe, Mn, and Zn were lower in the HD trees than in the LD trees. But the total K content was higher in the HD trees, and its DOP index was sufficient, while the total K concentration was of a low and deficient level in LD trees. Moreover, the DOP indices for total Ca, Mn, and Zn were less deficient in LD trees than in HD trees, and the overall nutrient imbalances were exacerbated in HD trees. The RED was significantly lower than RES for the total N, P, S, Cu, Fe, and Zn but significantly higher than RES for K. This study underscores the significant influence of MTTD on the mineral nutrition of KP mangoes, revealing distinct nutrient variations between trees with low and high MTTD infection levels. These findings have important implications for mango crop management, emphasizing the need for targeted nutrient interventions to address imbalances induced by MTTD and enhance the overall health and resistance of mango trees against MTTD infections. [ABSTRACT FROM AUTHOR]

Abstract A crucial determining factor in agricultural productivity is biotic stress. In addition, supply of quality food to the ever-increasing world’s population has raised the food demand tremendously. Therefore, enhanced agricultural crop productivity is the only option to mitigate these concerns. It ultimately demanded the often and indiscriminate use of synthetic agrochemicals such as chemical fertilizers, pesticides, insecticides, herbicides, etc. for the management of various biotic stresses including a variety of plant pathogens. However, the food chain and biosphere are severely impacted due to the use of such harmful agrochemicals and their byproducts. Hence, it is need of hour to search for novel, effective and ecofriendly approaches for the management of biotic stresses in crop plants. Particularly, in plant disease management, efforts are being made to take advantage of newly emerged science i.e. nanotechnology for the creation of inorganic nanoparticles (NPs) such as metallic, oxide, sulphide, etc. through different routes and their application in plant disease management. Among these, green nanomaterials which are synthesized using environmentally friendly methods and materials reported to possess unique properties (such as high surface area, adjustable size and shape, and specific functionalities) making them ideal candidates for targeted disease control. Nanotechnology can stop crop losses by managing specific diseases from soil, plants, and hydroponic systems. This review mainly focuses on the application of biologically produced green NPs in the treatment of plant diseases caused due to bacteria, viruses, and fungi. The utilization of green synthesis of NPs in the creation of intelligent targeted pesticide and biomolecule control delivery systems, for disease management is considered environmentally friendly due to its pursuit of less hazardous, sustainable, and environmentally friendly methods.

Utilizing Wireless Sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100 % F1-score, 100 % recall, 100 % precision, and 100 % accuracy; ii) the Rice Seedling dataset with 99.5 % F1-score, 99.5 % recall, 99.5 % precision, and 99.5 % accuracy; and iii) the Rice Varieties dataset with 97.99 % F1-score, 97.99 % recall, 98.14 % precision, and 97.99 % accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.

This study reviewed the advancements of Agriculture 4.0 in mechanized sugarcane harvesting, analyzing the efficiency of the mechanized harvesting system and evaluating the impact of the harvesting, loading, and transportation process costs. The mechanized sugarcane harvesting system in the 2020/21 harvest reached 91.6% or approximately 588 Mg in Brazil. As this activity demands a high amount of capital - assets, labor, inputs - the cost of the harvesting, loading, and transporting process represents 40% of the sugarcane production cost. The continuous delivery of raw material to the industrial processing unit is essential to ensure stability and maximum yield for obtaining sugar and ethanol, especially in an environment with geographical dispersion of harvesting fronts and agronomic factors linked to crop management. Therefore, tools that enable real-time equipment tracking benefit process management. Digital agriculture, defined as the application of technologies that seek to add intelligence and process automation, allows for improved agricultural management of farms, making it possible to reach new productivity levels in the field. The data generated through digital agriculture allows for the creation of models to construct scenarios that improve decision-making, both in terms of operational strategies - distribution of resources by harvesting front - and long-term strategies - the adoption of new technologies in the process. [ABSTRACT FROM AUTHOR]

Crop phenological information must be reliably acquired earlier in the growing season to benefit agricultural management. Although the popular shape model fitting (SMF) method and its various improved versions (e.g., SMF by the Separate phenological stage, SMF-S) have been successfully applied to after-season crop phenology detection, these existing methods cannot be applied to within-season crop phenology detection. This discrepancy arises due to the fact that, in the within-season scenario, phenological stages can beyond the defined cut-off time. Consequently, enhancing the alignment of the vegetation index (VI) curve segments prior to the cut-off time does not necessarily guarantee accurate within-season phenological detection. To resolve this issue, a new method named s patio t emporal s hape m odel f itting (STSMF) was developed. STSMF does not seek to optimize the local curve matching between the target pixel and the shape model; instead, it determines similar local VI trajectories in the neighboring pixels of previous years. The within-season phenology of the target pixel was thus estimated from the corresponding phenological stage of the determined local VI trajectories. When compared with ground phenology observations, STSMF outperformed the existing SMF and SMF-S which were modified for the within-season scenario ( SMF ws and SMFS ws ) with the smallest mean absolute differences (MAE) between observed phenological stages and their corresponding model estimates. The MAE values averaged over all phenological stages for STSMF, SMFS ws , and SMF ws were 9.8, 12.4, and 27.1 days at winter wheat stations; 8.4, 14.9, and 55.3 days at corn stations; and 7.9, 12.4, and 64.6 days at soybean stations, respectively. Intercomparisons between after-season and within-season regional phenology maps also demonstrated the superior performance of STSMF (e.g., correlation coefficients for STSMF and SMFS ws are 0.89 and 0.80 at the maturity stage of winter wheat). Furthermore, the performance of STSMF was less affected by the detection time and the determination of shape models. In conclusion, the straightforward, effective, and stable nature of STSMF makes it suitable for within-season detection of agronomic phenological stages. [ABSTRACT FROM AUTHOR]

In-situ leaf chlorophyll content (LCC) estimation based on hyperspectral imaging (HSI) is crucial to track the growth status of crops for field management. However, spatial and spectral features of HSI data, suffering from interference of growth dynamic effect and soil, pose the challenge on accuracy and robustness of LCC estimation in several years and growth stages. Therefore, a joint spectral-spatial feature extraction method was proposed by cascade of three-dimensional convolutional neural network (3DCNN) and long short-term memory (LSTM) to reduce the interference for optimising the LCC estimation. Firstly, crop pixels were separated from soil with vegetation index segmentation method. Secondly, when raw images and segmented pixels were input, sensitive bands were selected by random frog (RF bands), and 3DCNN-LSTM was used to extract the joint spectral-spatial features. Finally, models established by RF bands, 3DCNN and 3DCNN-LSTM were compared, and robustness in individual years and stages was validated. Results showed that RF bands and 3DCNN obtained R P 2 of 0.76 and 0.84 when not segmented. After segmentation, performance of 3DCNN improved (R P 2 = 0.85) compared to RF bands (R P 2 = 0.80). Spectral-spatial features by 3DCNN reduced the interference of soil. 3DCNN-LSTM without and with segmentation obtained good performance with R P 2 of 0.95 and 0.96, and the proposed method could reduce the image segmentation process. The optimal model achieved R P 2 above 0.93 in individual years (R P 2 = 0.96 in 2021, R P 2 = 0.94 in 2021) and R P 2 in the range of 0.87–0.97 at individual stages. This paper provides a method to track growth variability between soil and crop for the LCC estimation optimisation. • 3DCNN-LSTM method was proposed to extract spectral-spatial feature for LCC estimation. • 3DCNN reduced the interference of soil and crop difference. • 3DCNN-LSTM method can reduce the image segmentation procedure. • LSTM captured growth dynamics and improved robustness in multiple years and stages. [ABSTRACT FROM AUTHOR]

In this study, a novel microwave sensing system, consisting of a microstrip self-resonant spiral coil inductively coupled to an external concentric planar probe loop, is presented and applied to the non-destructive detection of morpho-physiological plant responses to water stress. The optimised set-up of the proposed sensor ensures a highly sensitive spiral coil, which is a fundamental requirement to derive accurate information on plants' behavioural alterations related to water stress conditions. The proposed microwave sensor was tested it on two potted maize cultivars (Zea mays L.), namely " Cinquantino Bianchi " (CB) and " Scagliolo Frassine " (SF). For each cultivar, half of the samples were maintained at 100% (T100) field capacity while the other half was at 25% (T25) from 46 to 74 Days After Sowing (DAS). The frequency (f r) shift and the amplitude peaks variation of the real component of the external planar probe input impedance (ℜ(Z i n p u t )) were obtained daily by positioning the sensor on the stem. These measured data were related to morpho-physiological parameters destructively acquired at four different growth stages. The resulting linear correlation between the stem's freshwater content ( F W C s t e m ) with both f r (r > −0.64) and the amplitude peaks (ℜ (Z i n p u t )) (r > -0.70) provided evidence of the sensor's ability to identify stem dielectric properties' variations between the two water treatments. Concurrently, the sensor response demonstrated the capability to identify changes in the morphology and histology of the stem. Based on preliminary findings, the proposed sensor shows potential for employment in the real-time monitoring of plant water status, contributing to more economically and environmentally sustainable crop management practices. While the current correlations between plant water content and sensor measurements require further refinement to meet the rigorous industrial standards, nevertheless a large-scale adoption can be envisioned by leveraging IoT methodologies. • A novel sensor to non-destructively decode plant water stress responses is proposed. • Stress-induced resonance frequency and amplitude shifts in the stem signal recorded. • Stem dielectric properties were highly responsive to water content variations. • Variety-dependent adjustments of maize stem anatomy to water stress detected. • Sensor offers a valid solution for real-time monitoring of plant water needs. [ABSTRACT FROM AUTHOR]

A field experiment was carried out to evaluate the effect of tillage and residue management practices on grain yield, economics, and soil fertility in a RWCS. The results inferred that wheat sown with happy seeder (full residue retained)-ZWW recorded 11.5% higher grain yield as compared to zero tillage sowing (residue removed)-ZWWO and 10.9% higher than farmer's practice (TRWO-CWWO, residue removed and sowing with seed cum fertilizer drill after preparatory tillage). Retention or incorporation of paddy residues produced 16.0% higher grain yield than its removal or burning simultaneously improving soil health. Puddled transplanted rice (PTR) with or without wheat stubble recorded a 33.2% significantly higher yield than zero tillage direct seeded rice (ZTDSR) without wheat stubble. System productivity in TRW-ZWW treatment was 15 and 6.8% higher than zero residue treatments and only rice residue retained/incorporated treatments, respectively, and resulted in additional returns of Rs. 28,871 ha−1 as compared with conventional RWCS. There was a significant (p <.05) improvement in soil organic carbon in residue retained/incorporated plots. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of soil phosphorus (P) over extended periods of soil and crop management practices is essential for sustainable P management. This study assessed the impact of four crop rotations [maize-wheat (M-W), maize-wheat-mungbean (M-W-Mb), maize-wheat-maize-chickpea (M-W-M-C), and pigeonpea-wheat (P-W)] each with three levels of nutrient management [control (CT), integrated nutrient management (suboptimal fertilizer+ farmyard manure + crop residue + bio-fertilizers; INM), and sole-chemical fertilizers (CF)] on soil inorganic and organic P (Pi and Po) pools and crop–soil relationships at the end of 15-year cropping. Legume-inclusive rotations resulted in higher labile-P pools, being higher with M-W-M-C and P-W. M-W-M-C rotation had higher moderately labile organic P (Po) in both surface (+23%) and subsurface (+18%) depths over M-W. Di-calcium P (Ca2-P) (+6%) and microbial biomass P (MBP) (+46%) increased in M-W-Mb over M-W, while iron-P (Fe-P) was reduced (−12%) in subsurface soil. INM enhanced bioavailable-P [soluble-P (+17%), labile-Pi (+15%) labile-Po (+12%), MBP (+96%), moderately labile-Po (+22%)] over CF in surface soil, while CF had a higher Fe-P (+16%). Legumes in rotation caused notable changes in the surface-to-subsurface ratio (SSBR) of Ca2-P (1.18–1.50) and occluded-P (1.64–2.78). INM had a higher SSBR of labile-P pools but had a lower SSBR of occluded-P facilitating mobilization of the later in the surface. Hence, in tropical soils, legume-inclusive diversification (particularly with chickpea) and INM involving crop residue recycling could be a sustainable option to improve P use efficiency, crop productivity, and save fertilizer resources. [ABSTRACT FROM AUTHOR]

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators—bioavailable P, bioavailable Zn, leachable Pb, and Sr—were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model’s prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency. [ABSTRACT FROM AUTHOR]

Climate change affects changes in rice-based systems of coastal Bangladesh. Both external pressures and system-immanent attributes influence farmers' adoption of new production practices, leading to the emergence of new land use patterns. Field and household surveys quantified recent and emerging change trends in crop yields and associated intensification-related practices in representative rice-based systems, using a diachronic approach (comparing the years 2011 and 2021). We analyzed 240 rice-producing farms, representing three rice-based cropping systems, namely extensive rice-fallow rotations, intensified irrigated rice double rotations, and diversified rice-mungbean rotations. The study sites contrasted favorable biophysical conditions (climate, soil type, water source and quality, soil fertility) in the Barishal district, and marginal (unfavorable biophysical environments) conditions at Patuakhali district in coastal Bangladesh. Soil fertility attributes were assessed at both study sites. The type, the extent, and the pathways of recent changes differed between locations, systems and seasons. We observed significant increases in aggregated yield (across crops and seasons), in individual crop yields, and in economic yields at both the marginal and the favorable sites. Crop yields varied widely (rice: 3.0-7.8 t ha-1, mungbean: 0.4-1.5 t ha-1), and rice yield was higher in dry than in the wet season. Farmers' adoption of intensification practices started earlier in the marginal study area, but the extent of the changes was larger at favorable sites. Most prominent was the mechanization of tillage operations and an increased use of mineral fertilizers, with the largest changes observed in irrigated rice and in dry season mungbean. Such site-, system-, and season-specific assessments will permit identifying drivers of change and can inform the assessment of potential future patterns of land system changes. [ABSTRACT FROM AUTHOR]

Deliberate open burning of crop residues emits greenhouse gases and toxic pollutants into the atmosphere. This study investigates the environmental impacts (global warming potential, GWP) and economic impacts (net cash flow) of nine agricultural residue management schemes, including open burning, fertilizer production, and biochar production for corn residue, rice straw, and sugarcane leaves. The environmental assessment shows that, except the open burning schemes, fossil fuel consumption is the main contributor of the GWP impact. The fertilizer and biochar schemes reduce the GWP impact including black carbon by 1.88–1.96 and 2.46–3.22 times compared to open burning. The biochar schemes have the lowest GWP (− 1833.19 to − 1473.21 kg CO2-eq/ton). The economic assessment outcomes reveal that the biochar schemes have the highest net cash flow (222.72—889.31 US$2022/ton or 1258.15–13409.16 US$2022/ha). The expenditures of open burning are practically zero, while the biochar schemes are the most costly to operate. The most preferable agricultural residue management type is the biochar production, given the lowest GWP impact and the highest net cash flow. To discourage open burning, the government should tailor the government assistance programs to the needs of the farmers and make the financial assistance more accessible. [ABSTRACT FROM AUTHOR]

BACKGROUND: Cassava is an important crop for the survival of smallholder farmers in Cameroon. However, the cassava sector has a low production per unit area compared to the technological potential in this country. In this context, breeders have developed varieties based mainly on their potential in terms of yield and disease resistance. These varieties have been widely disseminated in Cameroon within the framework of development projects. However, these releases have not achieved the expected adoption and yield levels at the national level. Therefore, it appears important to rethink the determinants of dissemination with a broader examination of the cassava production system. RESULTS: This paper analyses varietal complementarity as a key strategy in support of optimizing the experimental and continuous use of cassava varieties by farmers in the Central and Eastern regions of Cameroon. These two regions account for 50% of the country's production. A total of 111 semi‐structured interviews were conducted with farmers selected through purposive sampling in four villages in Central and Eastern Cameroon where improved varieties have been disseminated. The research revealed four types of complementarity, related to use, crop management, risk management and cultural complementarity. CONCLUSION: Our results argue for considering varietal complementarities practiced by farmers, within research and development programs to develop more effective breeding and dissemination approaches. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

[Display omitted] • Erosion-based prioritization using a comprehensive approach is vital. • The Shazand sub-Watersheds, Iran, were prioritized using the game theory. • The Shazand sub-watersheds were prioritized differently in various scenarios. • The game theory-based Condorcet approach holistically and successfully ranked sub-watersheds. The soil erosion of the watershed is different in temporal and spatial scales due to complex climatic and environmental conditions and their dynamic state over time. Accordingly, it is necessary to prioritize sub-watersheds based on their dynamic conditions to reduce the destructive effects of soil erosion. However, this issue has to be sufficiently reported on the scale of the watershed. Towards that, a game theory-based prioritization approach was planned concerning changes in vegetation cover and rainfall in the Shazand Watershed of the Markazi Province, Iran. To achieve the purpose of the study, the amount of soil erosion was first estimated in different scenarios resulting from the changes in rainfall erosivity and vegetation cover using the RUSLE model. Next, pairwise comparisons between 24 sub-watersheds in all scenarios were made based on the sub-watersheds' ranks in soil erosion in each scenario. Considering all scenarios, the priority of the sub-watersheds was assessed using the Condorcet approach. The results showed that the mean soil erosion in the watershed ranged from 5.4 and 36 t ha−1 y−1 while applying various scenarios. It is worth mentioning that the prioritization results obtained from the Condorcet algorithm and the current soil erosion rate are highly similar. On the other hand, sub-watersheds 2 and 9 were selected as the first and last priority, providing an appropriate roadmap for managers and relevant departments to adopt appropriate soil erosion inhibition measures. [ABSTRACT FROM AUTHOR]

Interest in improving the long-term sustainability of agricultural production systems has focused on identifying management practices that promote soil health. No tillage, cover cropping, and amending soils with broiler (Gallus gallus domesticus L.) litter are commonly adopted conservation practices that have been shown to improve soil fertility and crop yield. However, the overall influence of these conservation practices on soil health in the southeastern US are not well understood. Thus, a study was conducted to evaluate the influence of tillage, broiler litter (BL) applications, and cropping systems on soil biochemical properties. Soils were collected from field research plots under long-term management (>than 25 years of tillage, 15 years of broiler litter application, and 15 years of cropping system). Soil microbial biomass, C, N, and P, amidohydrolases, and dissolved organic matter (DOM) were evaluated as indicators of soil health. Adopting tillage and BL into the agricultural management system modified the biochemical parameters of the soils evaluated. Most of these modifications occurred in the 0–5 cm depth. Higher microbial biomass carbon (MBC; 85%) and nitrogen (MBN; 10%) and enzyme activities of asparaginase (65%) and glutaminase (70%) were observed in the 0–5 cm depth under no tillage (NT) compared to conventional tillage (CT), indicating greater biological activities were established in these soil ecosystems. Broiler litter applications increased microbial biomass N and activities of asparaginase and glutaminase in both soil depths. In addition, microbial biomass phosphorus (MBP) was increased following BL application in the 0–5 cm depth. The results suggest that long-term management of NT and BL additions can improve the health of eroded southeastern US soils by altering the soil biochemical parameters. [ABSTRACT FROM AUTHOR]

Agriculture is considered the leading field around the world, which is also the backbone of India. Agriculture is in a flawed state because the temperature changes, along with their uncertainty, cause huge damage to the crops during the manufacturing process. So, the appropriate prediction of crop expansion plays a vital role in the management of crop growth. This prediction can enhance the federated industries to make their sustainability toward the occupation. Recently, the farmers have not selected suitable crops for their cultivation based on soil factors. This makes a negative impact on crop yield, and thus, the Indian farmers can suffer from severe losses besides the monetary front. Hence, the optimal crop recommendation model has to consider different parameters of the soil for forecasting the best crop for cultivation, which increases crop growth and crop production. Thus, this research work explores a new crop recommendation model for precision agriculture intending to promote crop yield and alleviate the loss to farmers. Initially, this research work gathers the standard data regarding the agricultural parameters of some areas. Then, the deep features using an autoencoder, and statistical features are gathered along with the Principal Component Analysis (PCA)-based features. Next, all three sets of features are fused and fed to the developed Adaptive Henry Gas Solubility Optimization (AHGSO) for selecting the optimal features. Finally, the chosen optimal features are fed to the recommendation stage, where a Gated Recurrent Unit with Ridge Classifier (GRU-RC) is suggested for getting the precise outcome regarding the recommended crop suitable to that agricultural parameter. Here, the optimal solutions are attained by tuning the parameters of GRU and ridge classifier with the same I-HGSO. At last, the results obtained from the hybrid method can be considered more efficient. [ABSTRACT FROM AUTHOR]

Crop productivity, soil quality and fertility have been degraded due to intensive conventional tillage and poor crop management practices. Conservation tillage along with proper crop establishment methods (including seed priming, integrated nutrient management) helps in sustaining the productivity of crops, especially under rainfed conditions. A field experiment was carried out to evaluate the effect of seed priming, tillage and nutrient management practices on nutrient balance; chemical and microbial properties of soil; and productivity of maize-wheat cropping system in rainfed conditions. The experiment was laid out in factorial randomized block design with three factors (tillage, seed priming and nutrient management) and control as independent treatment for general comparison of results. The findings showed that chemical and microbial properties of soil were enhanced under zero tillage+ mulch as compared to other tillage practices. However, crop productivity was higher in conventional tillage+ mulch (70.28 q/ha (maize), 35.27 q/ha (wheat)). Under nutrient management practices, integrated nutrient management proved to be better as compared to recommended dose of fertilizers. Highest production efficiency (32.8 kg ha−1 day−1) was found under CT+M,Hydro,Int. Nutrient balance (N, P, K) was found higher in treatments with integrated nutrient management. [ABSTRACT FROM AUTHOR]

Addressing agricultural water scarcity poses a current challenge of growing concern, exacerbated by climate change. This is particularly relevant for stone fruit trees, such as apricot, cultivated in semi-arid zones, where regulated deficit irrigation (RDI) strategies are gaining attention to tackle the challenge. The RDI method involves optimizing various factors based on how the plant responds physiologically to indicators of its water needs. Among these indicators, water potential is considered the most reliable and influential measure. For numerous apricot varieties and diverse geographic locations, research consistently shows that implementing water reduction strategies during non-critical developmental stages of floral bud development or fruit growth does not significantly impact crop yield. However, it does lead to reduced vegetative growth, which could offer additional benefits in crop management. Furthermore, the implementation of RDI strategies leads to advantageous improvements in fruit quality, particularly storage capacity and morphometric and chemical fruit characteristics, such as total soluble solids content. This scoping review study suggests that RDI is a feasible strategy to address water scarcity in apricot cultivation; however, further studies focused on continuous water monitoring alternatives are necessary to optimize RDI techniques. Future research should prioritize optimizing RDI for different growth stages, exploring advanced technologies for precise implementation, and assessing environmental impacts, while addressing research gaps including the influence of climate variability and the interaction with other agronomic practices, to refine RDI strategies and enhance apricot orchard sustainability and productivity. [ABSTRACT FROM AUTHOR]

Evaluation of the net ecosystem exchange (NEE) of agroecosystems may help to assess regional carbon cycles and ensure carbon neutrality. However, farmland NEE is influenced by climate change, phenological period, and crop management. Therefore, in this study, we investigated the time-lag effects of climate change on the phenology and NEE of winter wheat, as well as the influence of crop management. We found that the impact of climate change on the phenology of winter wheat exhibits a time lag of 1 to 2 months, with notable spatial and temporal heterogeneity. When accounting for time-lag effects, the correlation between climate change and both phenology and NEE significantly strengthens, with the lagged impact on NEE primarily mediated through changes in phenology. Specifically, climate change accelerates the green-up and heading dates but delays the maturity date, while crop management extends the phenological period. Our findings indicated that during the green-up stage, NEE is mainly influenced by temperature changes resulting from climate change. During the heading stage, it is predominantly affected by crop management, and during the maturity stage, it is impacted by both factors, with crop management having a more significant effect. Overall, the accelerated green-up stage and delayed heading and maturity stages contribute to enhanced carbon sequestration. This research provides new insights into carbon exchanges in agricultural ecosystems. [ABSTRACT FROM AUTHOR]

Nitrogen (N) as a pivotal factor in influencing the growth, development, and yield of maize. Monitoring the N status of maize rapidly and non-destructive and real-time is meaningful in fertilization management of agriculture, based on unmanned aerial vehicle (UAV) remote sensing technology. In this study, the hyperspectral images were acquired by UAV and the leaf nitrogen content (LNC) and leaf nitrogen accumulation (LNA) were measured to estimate the N nutrition status of maize. 24 vegetation indices (VIs) were constructed using hyperspectral images, and four prediction models were used to estimate the LNC and LNA of maize. The models include a single linear regression model, multivariable linear regression (MLR) model, random forest regression (RFR) model, and support vector regression (SVR) model. Moreover, the model with the highest prediction accuracy was applied to invert the LNC and LNA of maize in breeding fields. The results of the single linear regression model with 24 VIs showed that normalized difference chlorophyll (NDchl) had the highest prediction accuracy for LNC (R2, RMSE, and RE were 0.72, 0.21, and 12.19%, respectively) and LNA (R2, RMSE, and RE were 0.77, 0.26, and 14.34%, respectively). And then, 24 VIs were divided into 13 important VIs and 11 unimportant VIs. Three prediction models for LNC and LNA were constructed using 13 important VIs, and the results showed that RFR and SVR models significantly enhanced the prediction accuracy of LNC and LNA compared to the multivariable linear regression model, in which RFR model had the highest prediction accuracy for the validation dataset of LNC (R2, RMSE, and RE were 0.78, 0.16, and 8.83%, respectively) and LNA (R2, RMSE, and RE were 0.85, 0.19, and 9.88%, respectively). This study provides a theoretical basis for N diagnosis and precise management of crop production based on hyperspectral remote sensing in precision agriculture. [ABSTRACT FROM AUTHOR]

Background and Aims: Selection effect (SE) and complementarity effect (CE) are often considered as two key indicators to mediate the positive diversity-productivity relationship in the maize-legume intercropping system, as intercropping generally enables crops to use the available resources more efficiently than monoculture. Yet, it is unclear whether soil moisture drives the SE and CE transformation, plant-plant interaction, and accordingly affect the productivity. Methods: A two-year field experiment was conducted to explore the impacts of soil water status (with and without plastic mulching) on interspecific interaction and productivity and its key mechanism in the maize-faba bean intercropping system. Results: Low soil moisture reduced the net effect (NE) and land equivalent ratio (LER) with evident yield loss (NE < 0 and LER = 1.05), whereas the improved soil moisture reversed this trend and resulted in overyielding (NE > 0 and LER = 1.13). Regardless of soil moisture, faba bean was the dominant species and maize was the inferior one. Low soil moisture promoted the contribution of SE to productivity (P < 0.05). In contrast, high moisture turned to lower the competition intensity of faba bean against maize and accordingly promote the transformation of biodiversity effect from SE to CE. The main mechanism was that the water competition imposed by faba bean strips on maize ones decreased the NE through reducing photosynthetic rate and water use efficiency of maize. Conclusion: The adaptive transitions of interspecific relationship and the biodiversity effect were driven by soil water availability. This novel phenomenon should be emphasized in the crop diversity management of intercropping system in the rainfed region. [ABSTRACT FROM AUTHOR]

Crop simulation models are valuable tools for decision making regarding evaluation and crop improvement under different field conditions. CSM-CROPGRO model integrates genotype, environment and crop management portfolios to simulate growth, development and yield. Modeling the safflower response to varied climate regimes are needed to strengthen its productivity dynamics. The main objective of the study was to evaluate the performance of DSSAT-CSM-CROPGRO-Safflower (Version 4.8.2) under diverse climatic conditions. The model was calibrated using the field observations for phenology, biomass and safflower grain yield (SGY) of the year 2016–17. Estimation of genetic coefficients was performed using GLUE (Genetic Likelihood Uncertainty Estimation) program. Simulated results for days to flowering, maturity, biomass at flowering and maturity and SGY were predicted reasonably with good statistical indices. Model evaluation results elucidate phenological events with low root mean square error (6.32 and 6.52) and high d-index (0.95 and 0.96) for days to flowering and maturity respectively for all genotypes and climate conditions. Fair prediction of safflower biomass at flowering and maturity showed low RMSE (887.3 and 564.3 kg ha−1) and high d-index (0.67 and 0.93) for the studied genotypes across the environments. RMSE for validated safflower grain yield (101.8 kg ha−1) and d-index (0.95) depicted that model outperformed for all genotypes and growing conditions. Longer appropriate growing conditions at NARC-Islamabad took optimal duration to assimilate photosynthetic products lead to higher grain yield. Safflower resilience to different environments showed that it can be used as an alternate crop for different agroecological regions. Furthermore, CROPGRO-Safflower model can be used as tool to further evaluate inclusion of safflower in the existing cropping systems of studied regions. [ABSTRACT FROM AUTHOR]

Agricultural productivity and sustainable crop management are pivotal aspects of global food security and economic stability. The quest for efficient and environmentally friendly practices in crop production has led to a surge in research exploring the utilisation of organic materials alongside conventional fertilisers to enhance crop growth and yield. An experiment was conducted over a growing period of three months, starting in August 2023 at the research field of Kwame Nkrumah University of Science and Technology, Kumasi-Ghana, to investigate the effects of Albizia zygia leafy biomass in combination with NPK fertiliser on the growth and yield of sweet pepper (Capsicum annum L.). A randomised complete block design was employed with four treatment groups replicated four times, including various combinations of A. zygia leafy biomass and NPK fertiliser, alongside control groups of sole fertiliser application and untreated plots (T1). The treatments were as follows: T1 = No biomass, no NPK (control); T2 = 0.5348 kg of A. zygia leafy biomass per 4.86 m2 (1100.5 kg/ha of A. zygia leafy biomass); T3 = 0.2463 kg of NPK per 4.86 m2 (506.7 kg/ha of NPK); and T4 = 0.2674 kg of A. zygia leafy biomass per 4.86 m2 + 0.1231 kg of NPK (550.3 kg/ha of A. zygia leafy biomass + 253.4 kg/ha of NPK). The assessed parameters were plant height, number of leaves, number of branches, fruit yield and fresh fruit weight. Significant variations (p < 0.05) were observed in the growth parameters (plant height, number of leaves, number of branches) and yield attributes (fruit yield and fresh fruit weight) of sweet pepper among the treatments. The combined application of A. zygia leafy biomass and NPK fertiliser (T4) exhibited increased effects on plant height, number of leaves, number of branches, fruit yield and fresh fruit weight compared to individual treatments and control groups (p < 0.05). Notably, the treatment combining A. zygia leafy biomass and NPK fertiliser (T4) demonstrated a substantial increase in plant height (44.58 cm), a significant improvement in the number of leaves (42.98) and a remarkable enhancement in the number of branches (11.33) compared to the sole applications and the control group. Furthermore, the combined treatment significantly increased (p < 0.05) the yield parameters, including fruit yield and fresh fruit weight per hectare, showcasing an increase of 8796/ha and 23.91 t/ha, respectively, compared to sole fertiliser application and the control. The findings from this study demonstrated that the incorporation of A. zygia leafy biomass, either solely or in combination with NPK fertiliser, positively impacted the growth and yield of sweet pepper plants. These results highlight the potential of A. zygia leafy biomass as a valuable organic amendment for sustainable and enhanced crop production, offering promising implications for agricultural practices aimed at improving yield and plant growth. [ABSTRACT FROM AUTHOR]

Global wheat production has faced, and will persist in encountering many challenges. Therefore, developing a dynamic cultivation approach generated through modeling is crucial to coping with the challenges in specific districts. The modeling can contribute to achieving global objectives of farmers' financial independence and food security by enhancing the cropping systems. The current study aims to assess the effects of cultivars and sowing windows intricately on irrigated wheat production using the two models from Coupled Model Intercomparison Project Phase 6 (CMIP6), including ACCES-CM2 and HadGEM31-LL under two shared socioeconomic pathways (SSP245, and SSP585). A two-year on-farm experiment was conducted for parametrization and validation of the APSIM-Wheat model at two locations. The model reasonably simulated the days to anthesis, maturity, biomass production, and yield within all cultivars. The normalized root-mean-square error (RMSE) of the phenological stages was simulated and measured values were 5% and 2–4%, while the index of agreement (IOA) was in the range of 0.84–0.88 and 0.95–0.97. An acceptable agreement of the simulated biomass (RMSE = 5–7% and 0.91–0.78) and yield (RMSE = 6–11% and IOA = 0.70–0.94) was identified in the model. Afterward, the LARS-WG model generated the baseline (2000–2014) based on the weather data at the sites and projected the models for the near (2030–2049) and remote future (2050–2070). The models revealed that not only the average maximum and minimum temperatures will rise by 1.85 °C and 1.62 °C which will exacerbate the reference evapotranspiration (ET0), but also the precipitation and solar radiation will reach + 58%, and + 0.25 Mj m−2. Our results clearly showed that precipitation volume over the growing seasons would elevate approximately two times as much as the baseline in the future, while there is a significant decrease in water productivity (WP) and yield from the intensive ET0. Based on the wheat simulation, the short-duration cultivar (Kalate) combined with the postponed planting (16-Dec) was determined as a practical alternative; nonetheless, both WP and yield significantly decreased by 40% and 7%, respectively (p < 0.05). In conclusion, identifying and analyzing future farming conditions (e.g., agro-climate, soil and crop management data) would provide a perception of the forthcoming scenarios. When applied, this knowledge can potentially mitigate the adverse impacts of climate change on global wheat production. [ABSTRACT FROM AUTHOR]

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with locationspecific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and rededge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status. [ABSTRACT FROM AUTHOR]

Citrus tristeza virus (CTV) is a highly destructive viral pathogen posing a significant threat to citrus crops worldwide. Disease management and crop protection strategies necessitate the development of rapid and accurate detection methods. In this study, we employed Oxford Nanopore sequencing to detect CTV in Citrus unshiu samples. Subsequently, we developed a specific and sensitive detection assay combining CRISPR/Cas12a with reverse transcription-recombinase polymerase amplification. The CRISPR-Cas12a assay exhibited exceptional specificity for CTV, surpassing conventional RT-PCR by at least 10-fold in sensitivity. Remarkably, the developed assay detected CTV in field samples, with zero false negatives. This diagnostic approach is user-friendly, cost-effective, and offers tremendous potential for rapid onsite detection of CTV. Therefore, the CRISPR-Cas12a assay plays a significant role in managing and preserving citrus trees that are free from viruses in the industry. [ABSTRACT FROM AUTHOR]

The continued expansion of the world population, increasingly inconsistent climate and shrinking agricultural resources present major challenges to crop breeding. Fortunately, the increasing ability to discover and manipulate genes creates new opportunities to develop more productive and resilient cultivars. Many genes have been described in papers as being beneficial for yield increase. However, few of them have been translated into increased yield on farms. In contrast, commercial breeders are facing gene decidophobia, i.e., puzzled about which gene to choose for breeding among the many identified, a huge chasm between gene discovery and cultivar innovation. The purpose of this paper is to draw attention to the shortfalls in current gene discovery research and to emphasise the need to align with cultivar innovation. The methodology dictates that genetic studies not only focus on gene discovery but also pay good attention to the genetic backgrounds, experimental validation in relevant environments, appropriate crop management, and data reusability. The close of the gaps should accelerate the application of molecular study in breeding and contribute to future global food security. [ABSTRACT FROM AUTHOR]

The increasing attention around cover crops in US agriculture to address many sustainability challenges necessitates targeted educational approaches to support future agriculture professionals. This article describes the process by which a team of educators, with different areas of expertise and from different regions, collaborated to design and implement a multi-university, semester-long, hybrid course titled “Cover Crops in Agroecosystems.” Course development occurred over 2 years during which the format, learning outcomes, as well as synchronized topics and activities were discussed, negotiated, and finalized. The course consisted of weekly pre-recorded lecture videos, in-person labs, and remote synchronous multi-site meetings. An end-of-semester survey administered at six universities in fall 2022 found that students valued the multi-regional dimension of the course, including learning from different instructors and interacting with students from other institutions. The survey also found broad student agreement that learning objectives were achieved, particularly around topics of cover crop agronomy and management. The team’s experience with course development highlights how designing and implementing a multi-university course where students report a quality learning experience is achievable. However, course execution ultimately required considerable effort, more than 2 years of planning, as well as ongoing team discussions and modifications that did not significantly reduce the time needed to prepare a more traditional individual instructor-led course. Course execution also required considerable flexibility in decision-making that aligned with different university constraints, instructor styles, and student needs. This effort and flexibility resulted in a quality student experience on a topic area of importance for advancing sustainable agriculture. [ABSTRACT FROM AUTHOR]