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Optimal foraging theory predicts that well‐defended potential foods should be exploited only when energy pay‐offs are great. Although stinging hymenopteran nests are both well‐defended and predated by primates, their larvae's energy yields rarely have been calculated, and predation‐linked foraging behaviours by primates infrequently documented. Based on 58 opportunistic observations of primates raiding wasp nests for larvae, we calculated energetic yields of low‐ and high‐risk wasp nest predation for Cebus albifrons, Saimiri collinsi, S. sciureus and Sapajus apella, and tested predictions derived from optimal foraging theory. We recorded how nests were processed and by which age‐sex classes, eaten nest fragment sizes, number of occupied and empty cells, and nest occupancy patterns (percent larvae/pupae, eggs, empty cells). Basal metabolic rate (BMR) calculations showed energetic yields from 15 min foraging on low‐risk nests (Polybia quadricincta) would meet energy needed to sustain adult female and male C. albifrons BMR for 4.9 and 4.5 h, respectively; yields from high‐risk (Chartergus artifex) nests for 6.5 and 6.2 h; Mischocyttarus sp. nest yields (low risk, but mimetically resembling other wasps) would meet S. collinsi BMR for 2.9 h (female) and 2.3 h (male), and 2.6 and 2.1 h, for the slightly larger S. sciureus, respectively. The Chartergus energetic‐yield value is nearly 20% of a 36 g chocolate bar (741 kJ). Our data provide quantitative support for the common assertion that wasp larvae and pupae are high‐yield foods for primates. As predicted by optimal foraging, energetic yield is sufficient to offset the risk and pain of being stung. [ABSTRACT FROM AUTHOR]

Soil is an important reservoir of innumerable natural and biological resources fundamental to the sustainability of life and the earth's functionality. The soil is complex due to changing biodiversity, physicochemical characteristics, disturbances, and pedogenesis, which are constituent indices required for the measurement of its healthiness. Hence, there is a need to concertedly protect the soil by consciously promoting practices and behaviors that optimize its priority functions in delivering ecosystem services. It is further significant for crop yield, hence the need to pay more attention to its health. Soil healthiness is also a reflection of its capacity to support biogeochemical processes, abiotic communities, and plant and animal productions. However, agronomic studies, until recently, focused more on the use of chemical indicators in determining soil health, despite the versatile ecophysiological role of microorganisms in soil formation, resource cycling, and management. These biological phenomena expressed by soil microbial communities form the basis for the conversion of diverse organic matters into bioutilizable resources for plants' healthy development. This review, therefore, explored the underlining mechanisms, particularly climate change-related, that caused divergent soil properties and how this impacted the microbial composition of healthy soil. Likewise, several pieces of agronomic literature on the physical characteristics, ecological services, and functions of a healthy soil were compared toward innovative best management practices for improving soil health. [ABSTRACT FROM AUTHOR]

The potential of agrobased paper mill effluent (PME) as ferti-irrigant was assessed. Ferti-irrigation responses to 5, 10, 25, 50, 75, and 100% of PME doses onPhaseolus vulgarisL., cv. Annapurna, in the rainy and summer seasons were investigated. The fertigant concentrations produced changes in electrical conductivity (EC), pH, organic carbon (OC), sodium (Na+), potassium (K+), calcium (Ca2+), magnesium (Mg2+), total Kjeldahl nitrogen (TKN), phosphate (PO43–), sulfate (SO42–), iron (Fe2+), cadmium (Cd), chromium (Cr), copper (Cu), manganese (Mn), and zinc (Zn) of the soil in both seasons. The agronomic performances ofP. vulgarisincreased from 5 to 25% in both seasons compared to controls. The accumulation of metals increased in soil andP. vulgarisfrom 5 to 100% PME concentrations in both seasons. The contamination factor (Cf) of various metals was in order of Cr > Mn > Cu > Cd > Zn for soil and Mn > Zn > Cu > Cd > Cr forP. vulgarisin both seasons after fertigation with PME. Therefore, PME can be used to improve the soil fertility and yield ofP. vulgarisafter appropriate dilution. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Bioconversion of paper mill bamboo and lime waste is a novel effort. [•] Solubility of heavy metals in paper mill wastes reduced due to vermiconversion. [•] Vermicomposted paper mill wastes improve soil health. [•] Vermicomposted paper mill wastes enhances crop yield. [Copyright &y& Elsevier]

In northern Laos, shortened fallows in the slash-and-burn upland rice system are not sustainable either from a production or economic standpoint. This paper evaluates the potential for using the indigenous fast-growing tree, Broussonetia papyrifera L. Vent. (paper mulberry) as an economic fallow crop. The objectives of this study are to assess the inner bark yield of paper mulberry, and to evaluate rice productivity following paper mulberry cultivation. In addition, allometric models for estimating the inner bark yield of paper mulberry are developed. Inner bark yield of paper mulberry stands ranging in age from 9 to 48 months ranged from 10 to 208 g m−2, and increased with age. The models using diameter at breast height and stem height, or diameter at breast height only as independent variables explained well the variation in inner bark yield. When paper mulberry was completely removed during the rice growing season, rice yields in these areas were similar to those following weedy bush fallow. When paper mulberry was allowed to grow, rice yields were negatively correlated to paper mulberry stem density. When paper mulberry lateral branches were pruned back, rice yields could be maintained if the paper mulberry stem density was <0.22 stems m−2, suggesting that if paper mulberry is managed properly, rice yields can be maintained at current levels. The paper mulberry-upland rice rotation system improves the productivity of a traditional slash-and-burn system through harvesting paper mulberry inner bark; however the long-term sustainability of such a system remains unclear and warrants further investigation. [ABSTRACT FROM AUTHOR]

Presents a study that investigated the effects of pulp and paper industry wastewater treatment sludge composts on soil and cereal crops in Finland. Chemical characteristics of composts prepared using forest industry sludges and limit values for soil improvers used in agriculture; Details of crop yield and nutrient intake of compost.

The present paper aims to give an overview of results collected in the Estonian Crop Research Institute related to the comparison between guttation and yield in grain crop cereals. The objects of research were oat variety 'Eugen' and wheat 'Manu', which went through the stages of germinating their seeds (in vitro), set them by sprouts down into the soil, and the emergence of sprouts under conditions of a hydrothermostat. Since transpiration in a hydrothermostat at an air temperature of 23 °C, and an extremely high value of air relative humidity was limited, therefore, due to this, the plant sprouts have begun to exude guttation fluid. As result, we have found that the amount of isolated gutted fluid correlates significantly with the grain yields of field trials indicators. The comparison between relative guttation and relative yield was described by a straightforward relationship. The entire experiment took about one week, and the first results of droplet prints on filter paper can be obtained after 60 hours. This was the novelty of our approach which provides the prerequisites for both increasing the reliability of conclusions regarding the yield obtained and its forecast. [ABSTRACT FROM AUTHOR]

We utilize a coupled economy–agroecology–hydrology modeling framework to capture the cascading impacts of climate change mitigation policy on agriculture and the resulting water quality cobenefits. We analyze a policy that assigns a range of United States government’s social cost of carbon estimates ($51, $76, and $152/ton of CO2-equivalents) to fossil fuel–based CO2 emissions. This policy raises energy costs and, importantly for agriculture, boosts the price of nitrogen fertilizer production. At the highest carbon price, US carbon emissions are reduced by about 50%, and nitrogen fertilizer prices rise by about 90%, leading to an approximate 15% reduction in fertilizer applications for corn production across the Mississippi River Basin. Corn and soybean production declines by about 7%, increasing crop prices by 6%, while nitrate leaching declines by about 10%. Simulated nitrate export to the Gulf of Mexico decreases by 8%, ultimately shrinking the average midsummer area of the Gulf of Mexico hypoxic area by 3% and hypoxic volume by 4%. We also consider the additional benefits of restored wetlands to mitigate nitrogen loading to reduce hypoxia in the Gulf of Mexico and find a targeted wetland restoration scenario approximately doubles the effect of a low to moderate social cost of carbon. Wetland restoration alone exhibited spillover effects that increased nitrate leaching in other parts of the basin which were mitigated with the inclusion of the carbon policy. We conclude that a national climate policy aimed at reducing greenhouse gas emissions in the United States would have important water quality cobenefits. [ABSTRACT FROM AUTHOR]

Agriculture stands as the bedrock of Malawi's economy, involving nearly 90% of the population in subsistence farming. However, the sector faces challenges arising from unpredictable weather patterns, climate shifts, and environmental factors that threaten its sustainability. This paper proposes a pioneering solution leveraging Machine Learning (ML) to address these challenges, presenting a robust decision support system for Crop Yield Prediction (CYP). By harnessing ML capabilities, the system aids in crucial decisions related to crop selection and management throughout the growing season, specifically tailored for the unique agricultural landscape of Malawi. This approach aims to empower farmers by providing valuable insights into soil quality, composition, and nutrients, enabling informed decisions to maximize crop yield. Through the integration of advanced technology into the agricultural domain, this paper seeks to usher in a transformative era for Malawian agriculture, fostering resilience and sustainability in the face of evolving environmental dynamics. [ABSTRACT FROM AUTHOR]

Highlights: [•] Bioconversion of tea factory and paper mill coal ash is a novel effort. [•] Solubility of Cr and Ni in coal ashes is reduced due to vermiconversion. [•] Vermicomposted coal ashes improve soil health. [•] Vermicomposted tea factory coal ash enhances crop yield. [Copyright &y& Elsevier]

Paper and pulp industries produce large quantities of wastewater which can have adverse effects on the receiving water systems. In the present study lysimeters were used and filled with different soils replicating natural soil horizons and provided with a leachate collection system. The physico-chemical characteristics of the soil in each lysimeter and the quality of wastewater before leaching were assessed. Treated wastewater was evaluated for crop irrigation, and was categorized according to the irrigation water class ‘Increasing Problem to Severe Problem’ with respect to salinity and specific ion toxicity. Sandy loam soils showed 96% chemical oxygen demand (COD) removal while clay loam soils removed 99% of COD, and the colour removal in both the cases was found to be 100%. Application of wastewater resulted in an increase of pH value, ranging from 6.2–7.6; the electrical conductivity (ECe) of saturated extracts was found to be 0.6–1.7 dS m−1, and exchangeable sodium percentage (ESP) ranged from 7.8–11.1% in soils. Similarly, an increase in the organic carbon, available nitrogen, phosphorus and potash content of soils was observed when irrigated with wastewater. Wastewater irrigation showed increased grain and straw yield of jowar, wheat and moong. These results permit successful utilization of pulp and paper mill wastewater for crop production without damaging the soils. [ABSTRACT FROM PUBLISHER]

Increased coverage when spraying, especially using contact substances, leads to higher efficiency and crops. Conventional nozzles, where the droplets move parallel to the stem of the plant ensures a good covering of the horizontal and inclined parts of plants, the vertical parts (strain) the deposit of the solution particles is lower and thus the coverage is inadequate. The main goal of this paper was to determine and to evaluate the coverage degree for 4 types of nozzles and for the Gamberini 500 orchard spraying machine. Experimental tests conducted with two types of nozzles aimed the coverage assessment of horizontal and vertical parts of the plant using conventional and double jet nozzles. Evaluation of coverage was made using water sensitive paper and the MATHCAD program, which determines the area covered. For the purpose of this paper, the machine Gamberini 500 was used in laboratory conditions to determine and evaluate the coverage degree. This spraying machine is a pneumatic one, with a special boom. In order to evaluate the degree of coverage, a special paper was used, which was placed at different heights (1; 2; 3m) [ABSTRACT FROM AUTHOR]

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research. Deep learning (DL) and machine learning (ML) models effectively deal with such challenges. This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024. In addition, it analyses the effectiveness of various input parameters considered in crop yield prediction models. We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield. The total number of articles reviewed for crop yield prediction using ML, meta-modeling (Crop models coupled with ML/DL), and DL-based prediction models and input parameter selection is 125. We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers. Each study is assessed based on the crop type, input parameters employed for prediction, the modeling techniques adopted, and the evaluation metrics used for estimating model performance. We also discuss the ethical and social impacts of AI on agriculture. However, various approaches presented in the scientific literature have delivered impressive predictions, they are complicated due to intricate, multifactorial influences on crop growth and the need for accurate data-driven models. Therefore, thorough research is required to deal with challenges in predicting agricultural output. [ABSTRACT FROM AUTHOR]

A field experiment with rain-fed winter wheat investigated the nutritional aspects of paper-mill sludge as a mulch and incorporated into the topsoil. Treatments with chemical fertilizers were also used as controls. Paper-mill sludge used as mulch with high rate (100MG ha-1) and also the combined N and K mineral fertilizer treatments increased yield when a low potassium otherwise caused potassium deficiency in wheat with high specific surface soil. High soil Ca: K molar ratio by incorporation lowered potassium uptake and yield, with visual symptoms of potassium deficiency. A very high Gapon selectivity coefficient (KG) for K exchange against Ca + Mg (16.58 (L/mole)0.5) produced a nonlinear normalized exchange isotherm in favor of potassium with these soils containing high illite. Ca and K which are released by sludge decomposition are diverged in soil whenmobilized by rain infiltration, lowering Ca : K molar ratio. Low soil Ca : K molar ratio may be expected by surface sludge application relative to incorporation, due to greater rain infiltration through upper soil layers and their effluent pore volumes per unit depth. Ca from triple superphosphate by the P, N, and K mineral fertilizers combined also reduced potassium uptake and yield relative to N and K combined. [ABSTRACT FROM AUTHOR]

Various plastic mulch (PE black, PE brown, and LDPE white), fiber mulch (Uco black woven), and paper mulch (Gropex gray) were examined in four perennial herb crops (Hyssopus officinalis, Melissa officinalis, Thymus vulgares, and Echinacea purpurea) in a field experiment during a three-year period in Norway. Over-wintering, total yield, herbs' nutrient content (N, P, K), soil temperature, and soil nitrogen were examined. For all species, the total yield was the highest when using dark-colored plastic mulches, followed by fiber mulch. The white plastic and paper did not return the same yield. The result was related to soil temperature and soil mineral N content. The over-wintering was only slightly affected by mulching. The concentration of K was significantly higher in herbs grown on dark-colored mulches compared with white plastic, paper, and bare soil, while no effect was found on N and P. [ABSTRACT FROM AUTHOR]

In this 5-yr study, application frequency of fresh (FDS) and composted (CDS) paper sludges was evaluated on soil properties and silage corn (Zea mays L.) and barley (Hordeum vulgare L.) yields and N uptake by comparing one application (2000) with repeated applications (2000, 2001, 2002). Treatments consisted of a control with neither amendment nor N fertilizer, a recommended N rate for corn (160 kg N ha-1) and barley (90 kg N ha-1), and FDS and CDS at a rate of 40Mg ha-1 on wet basis with or without reduced N fertilizer (120 kg N ha-1) on corn. Compared to the control, the FDS or CDS applications significantly increased the proportions of soil larger macroaggregates (>2mm) and the mean-weight diameter (MWD) of aggregates, regardless their application frequency. Single or three yrs additions of FDS or CDS produced, on average, an increase of 25 to 31% of the soil C and N contents compared to the control and the recommended N fertilizer treatment (160 kg N ha-1). In the first year of their application, the FDS or CDS with or without supplemental N fertilizer (120 kg N ha-1) reduced silage corn and N uptake, compared to the control probably due to N immobilization. However, silage corn yields were increased in the following years with one application or with repeated application of FDS or CDS. Results show that benefits on soil properties and crop yields of single application of FDS and CDS were similar to those of the triple applications and were still noticeable a few years following the last application. [ABSTRACT FROM AUTHOR]

This editorial summarizes the papers published in issue 17(1) (2022). This issue begins with a second editorial calling on researchers to publish replication results from previous studies. The first paper applies a spatiotemporal Bayesian hierarchical model for understanding the dynamics of second home ownership in Corsica. The second paper determines the optimal time to invest in a new airport using real options analysis. The third paper employs unit root tests to provide empirical evidence that environmental policy changes have not been effective up to now. The fourth paper provides empirical evidence that regional spillover effects should play a crucial role in the policy discussion about climate change. The fifth paper forecasts the direct impact of climate change on crop yields in the agricultural sector and the indirect impacts on other sectors of the Brazilian economy up to 2100. The sixth paper investigates whether the percentage of women in national parliaments positively affects public expenditures on social needs both internally and in neighbouring countries. The seventh paper sets out a general framework for store sales evaluation and prediction. [ABSTRACT FROM AUTHOR]

Paper production is among the industrial branches generating significant amounts of waste. Organic waste from paper manufacturing is mostly used in gardening and agriculture or converted for energy purposes, while inorganic waste, including green liquor dregs (GLD), is usually deposited on landfills. GLD has a highly alkaline reaction, suggestive of its possible utilization for land reclamation or for improvement of soil's physico‐chemical properties. The research results originate from a pot experiment with maize as the test plant. GLD was added to soil in doses 10–40 t ha−1. The trials consisted of two series: with and without tree bark (TB). GLD contributed to neutralization of the soil's acidic pH, enrichment of the sorption complex with alkaline cations, and to greater salinity of the soil. In the series with TB, it is demonstrated that GLD led to a decrease in the soil content of total organic carbon; while the content of total nitrogen increased, the C:N ratio is considerably narrowed. In response to GLD, the greenness of leaves significantly improves, but this does not translate into an increase in the yield of plants. Regarding the chemical composition of plants grown in soil treated with elevated GLD doses, the content of N and Na is demonstrably higher, although no such effect is discernible in the case of P and K. For the environment, safe doses of the applied waste, not causing significant deterioration of soil properties or depressing crop yields, are 10 and 20 t GLD ha−1. [ABSTRACT FROM AUTHOR]

The high cost of manual weed control and the overuse of herbicides restrict the yield and quality of soybean. Intelligent mechanical weeding and precise application of pesticides can be used as effective alternatives for weed control in the field, and these require accurate distinction between crops and weeds. In this paper, images of soybean seedlings and weeds in different growth areas are used as datasets. In the aspect of soybean recognition, this paper designs a YOLOv8nGP algorithm with a backbone network optimisation based on GhostNet and an unconstrained pruning method with a 60% pruning rate. Compared with the original YOLOv8n, the YOLOv8nGP improves the Precision (P), Recall (R), and F1 metrics by 1.1% each, reduces the model size by 3.6 mb, and the inference time was 2.2 ms, which could meet the real-time requirements of field operations. In terms of weed recognition, this study utilises an image segmentation method based on the Normalized Excess Green Index (NExG). After filtering the soybean seedlings, the green parts of the image are extracted for weed recognition, which reduces the dependence on the diversity of the weed datasets. This study combines deep learning with traditional algorithms, which provides a new solution for weed recognition of soybean seedlings. [ABSTRACT FROM AUTHOR]

To quantify the effect of weeds on the nitrogen (N) use, growth and yield of wet season paddy rice in the central and northern regions of Laos, we surveyed the paddy fields in these regions in October 1999 and November 2000. We found 13 weed species in total, but there were few major weeds abundant at the survey sites. In the infertile soils under rainfed conditions, weed growth was poor. Rough rice yield, the number of panicles, the number of seeds per square meter, the above-ground biomass of paddy rice and the amount of N accumulated in the above-ground biomass of paddy rice (amount of N in rice) were suppressed by competition with weeds. However, harvest index (HI) and nitrogen use efficiency (NUE) of paddy rice were not suppressed by competition with weeds. The amount of N in rice was suppressed by competition with weeds, the number of panicles decreased as the amount of N in rice decreased, and the number of seeds per square meter decreased as the number of panicles decreased. As a result, rough rice yield was suppressed by competition with weeds. The weeds competed with paddy rice for N uptake during the productive tillering stage. However, the ability of paddy rice to compete for N uptake with weeds was not reduced under rainfed lowland conditions. When the weeds were completely removed, the amount of N in rice increased. Rough rice yield may be increased by 10% under rainfed lowland conditions and by 17–19% under irrigated conditions. [ABSTRACT FROM AUTHOR]

The research made in the last 20 years resulted an almost linear increase in average grain/seed yield for different crops, associated with a change in grain/seed composition: decrease in the protein concentration at the expense of compounds resulting from carbon metabolism (starch, oil). Due to the rapid development of biological sciences and the scope of the means available in modern research this progress was achieved. It merits a reflection about the key processes limiting the plant productivity and about an adaptation to an environment, often very conservative. The integration of crop yield limiting factors into plant and canopy activities was made in order to highlight research objectives and new approaches. After a general analysis of yield increase mechanisms, the processes determining crop yield and grain composition and the effects of species, cultivars, and environmental factors (temperature, drought, nitrogen) were analysed. For this a conceptual model was established based on the source–sink relationship, especially during the post-anthesis period. In order to successfully improve the yield potential and the composition of grain and seeds, new knowledge of the processes is required concerning the intrinsic properties of the plant organs (seeds, leaves, roots) and their interactions. Several priority themes were considered. [Copyright &y& Elsevier]

Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production. [ABSTRACT FROM AUTHOR]

The quantification of ammonia (NH3) emissions is essential to the more accurate quantification of atmospheric nitrogen deposition, improved air quality and the assessment of ammonia-related agricultural policy and climate mitigation strategies. The quantity, geographic distribution and historical trends of these emissions remain largely uncertain. In this paper, a new Chinese agricultural fertilizer NH3 (CAF NH3) emissions inventory has been compiled that exhibits the following improvements: (1) a 1 km × 1 km gridded map on the county level was developed for 2008, (2) a combined bottom-up and top-down method was used for the local correction of emission factors (EFs) and parameters, (3) the spatial and temporal patterns of historical time trends for 1978-2008 were estimated and the uncertainties were quantified for the inventories, and (4) a sensitivity test was performed in which a province-level disaggregated map was compared with CAF NH3 emissions for 2008. The total CAF NH3 emissions for 2008 were 8.4 Tg NH3 yr-1 (a 6.6-9.8 Tg interquartile range). From 1978 to 2008, annual NH3 emissions fluctuated with three peaks (1987, 1996 and 2005), and total emissions increased from 3.2 to 8.4 Tg at an annual rate of 3.0 %. During the study period, the contribution of livestock manure spreading increased from 37.0 to 45.5 % because of changing fertilization practices and the rapid increase in egg, milk and meat consumption. The average contribution of synthetic fertilizer, which has a positive effect on crop yields, was approximately 38.3 % (minimum: 33.4 %; maximum: 42.7 %). With rapid urbanization causing a decline in the rural population, the contribution of the rural excrement sector varied widely between 20.3 and 8.5 %. The average contributions of cake fertilizer and straw returning were approximately 3.8 and 4.5 %, respectively, thus small and stable. Collectively, the CAF-NH3 emissions reflect the nation's agricultural policy to a certain extent. An effective approach to decreasing PM2.5 concentrations in China would be to simultaneously decrease NOx, SO2 and NH3 emissions. [ABSTRACT FROM AUTHOR]

Bird invasion will reduce the yield of high-value crops, which threatens the healthy development of agricultural economy. Sonic bird repellent has the advantages of large range, no time and geographical restrictions, and low cost, which has attracted people's attention in the field of agriculture. At present, there are few studies on the application of sonic bird repellents in pear orchards to minimize economic losses and prolong the adaptive capacity of birds. In this paper, a sound wave bird repellent system based on computer vision is designed, which combines deep learning target recognition technology to accurately identify birds and drive them away. The neural network model that can recognize birds is first trained and deployed to the server. Live video is captured by an installed webcam, and the sonic bird repellent is powered by an ESP-8266 relay switch. In a pear orchard, two experimental areas were divided into two experimental areas to test the designed sonic bird repellent device, and the number of bad fruits pecked by birds was used as an indicator to evaluate the bird repelling effect. The results showed that the pear pecked fruit rate was 6.03% in the pear orchard area that used the acoustic bird repeller based on computer recognition, 7.29% in the pear orchard area of the control group that used the acoustic bird repeller with continuous operation, and 13.07% in the pear orchard area that did not use any bird repellent device. While acoustic bird repellers based on computer vision can be more effective at repelling birds, they can be used in combination with methods such as fruit bags to reduce the economic damage caused by birds. [ABSTRACT FROM AUTHOR]

Undernutrition is a confront to the health and output of the populace. It is viewed as one of the five leading contrary health impacts of climate variability and is defined as different measures of nutritional status. We aimed to assess the scientific evidence base for the impact of climate variability on childhood undernutrition (particularly wasting and underweight) in low- and middle-income countries. A systematic review was conducted to identify the peer-reviewed and gray full-text studies in English with no limits for the year of publication and study design. This review covers only published studies from four databases (PubMed, Scopus, Web of Science, and Science Direct). The risk of bias was assessed using the ROVBIS tool in individual studies. The PRISMA Statement checklist for systematic reviews was referred for this review process. A significant correlation between climate variables, temperature, rainfall, and drought, and at least one undernutrition parameter in 19 out of 22 studies was observed in this systematic review. In addition, we note that crop yield, maternal education, nutritional status of mothers, wealth status at the household level, and individual levels also play substantial roles in mediating the nutritional impacts. The findings of our analysis imply that exposure to climate variables may be linked to an increased risk of undernutrition both during and for several years following climate events. This may imply that undernutrition is never caused by temperature, precipitation, drought, or other weather-related factors alone but rather that undernutrition is triggered in children who are already at risk. [ABSTRACT FROM AUTHOR]

A timely and consistent assessment of crop yield will assist the farmers in improving their income, minimizing losses, and deriving strategic plans in agricultural commodities to adopt import-export policies. Crop yield predictions are one of the various challenges faced in the agriculture sector and play a significant role in planning and decision-making. Machine learning algorithms provided enough belief and proved their ability to predict crop yield. The selection of the most suitable crop is influenced by various environmental factors such as temperature, soil fertility, water availability, quality, and seasonal variations, as well as economic considerations such as stock availability, preservation capabilities, market demand, purchasing power, and crop prices. The paper outlines a framework used to evaluate the performance of various machine-learning algorithms for forecasting crop yields. The models were based on a range of prime parameters including pesticides, rainfall and average temperature. The Results of three machine learning algorithms, Categorical Boosting (CatBoost), Light Gradient-Boosting Machine (LightGBM), and eXtreme Gradient Boosting (XGBoost) are compared and found more accurate than other algorithms in predicting crop yields. The RMSE and R2 values were calculated to compare the predicted and observed rice yields, resulting in the following values: CatBoost with 800 (0.24), LightGBM with 737 (0.33), and XGBoost with 744 (0.31). Among these three machine learning algorithms, CatBoost demonstrated the highest precision in predicting yields, achieving an accuracy rate of 99.123%. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) has brought about transformative changes across diverse industries, and its application in agriculture, particularly in soil management, has become a focal point of research and innovation. This paper explores the paradigm shift enabled by IoT in detecting organic carbon in soil, with the overarching goal of advancing agricultural productivity and sustainability. Emphasizing the pivotal role of organic carbon in soil health, the research addresses the existing challenges associated with its detection and underscores the potential advantages offered by IoT-based solutions for precise and real-time monitoring. The study meticulously examines various IoT technologies, including sensors, data analytics, and wireless communication protocols, within the specific context of organic carbon detection. A thorough review of contemporary techniques, methodologies, and devices utilized in IoT-based organic carbon detection in soil is presented. The paper not only sheds light on the current state-of-the-art but also explores the advantages, limitations, and prospects inherent in the implementation of IoT-enabled systems in agriculture. The envisioned outcomes encompass enhanced soil management practices, heightened crop yields, and the simultaneous preservation of the environment. In essence, this research contributes to the expanding body of knowledge surrounding IoT applications in agriculture, specifically emphasizing the transformative potential of IoT-enabled organic carbon detection for revolutionizing farming practices and promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Continued population growth and limited land availability will facilitate the utilization of plant growth regulators (PGRs) in sustainable agriculture to enhance crop yields. The PGRs industry has progressed significantly from 2003 to 2022, resulting in a surge of research activities in the field of PGRs. However, the existing studies lack the exploration of the industry trends, as well as the challenges and opportunities for innovation in PGR development. Here, we analyze the dynamic trends within the PGR industry by examining key factors such as the PGR market, patent applications, scientific papers, and PGRs registrations from 2003 to 2022. Additionally, we investigate the specific effects of major agrochemicals on plants. These data will provide essential insights into the ongoing evolution and future trends of PGRs. Importantly, it is crucial to actively pursue research and development (R&D) of a broader range of PGRs to respond to the current needs of the PGR market and drive further growth therein. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to improving agricultural practices using modern technologies such as machine learning, IoT, and image processing. The proposed system includes two main modules: fertilizer prediction and crop recommendation and crop disease detection. The first module utilizes machine learning algorithms to analyze data from various sources such as soil sensors, weather stations, and historical crop yield data to predict optimal fertilizer requirements and recommend suitable crops for the specific soil type and climate. The second module employs image processing and Convolutional Neural Network (CNN) algorithms to detect crop diseases by analyzing images captured by IoT devices such as drones and cameras. The proposed system aims to increase crop yield, reduce the cost of crop maintenance, and promote sustainable farming practices. The results of this research demonstrate the potential of smart agriculture technologies in improving food security and agricultural sustainability. [ABSTRACT FROM AUTHOR]

Accurate identification of crop diseases can effectively improve crop yield. Most current crop diseases present small targets, dense numbers, occlusions and similar appearance of different diseases, and the current target detection algorithms are not effective in identifying similar crop diseases. Therefore, in this paper, an improved model based on YOLOv5s was proposed to improve the detection of crop diseases. First, the CSP structure of the original model in the feature fusion stage was improved, and a lightweight structure was used in the improved CSP structure to reduce the model parameters, while the feature information of different layers was extracted in the form of multiple branches. A structure named CAM was proposed, which can extract global and local features of each network layer separately, and the CAM structure can better fuse semantic and scale inconsistent features to enhance the extraction of global information of the network. In order to increase the number of positive samples in the model training process, one more grid was added to the original model with three grids to predict the target, and the formula for the prediction frame centroid offset was modified to obtain the better prediction frame centroid offset when the target centroid falled on the special point of the grid. To solve the problem of the prediction frame being scaled incorrectly during model training, an improved DIoU loss function was used to replace the GIoU loss function used in the original YOLOv5s. Finally, the improved model was trained using transfer learning, the results showed that the improved model had the best mean average precision (mAP) performance compared to the Faster R-CNN, SSD, YOLOv3, YOLOv4, YOLOv4-tiny, and YOLOv5s models, and the mAP, F1 score, and recall of the improved model were 95.92%, 0.91, and 87.89%, respectively. Compared with YOLOv5s, they improved by 4.58%, 5%, and 4.78%, respectively. The detection speed of the improved model was 40.01 FPS, which can meet the requirement of real-time detection. The results showed that the improved model outperformed the original model in several aspects, had stronger robustness and higher accuracy, and can provide better detection for crop diseases. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in our society by providing food, fiber, and raw materials for various industries. The world's population is steadily growing, and there is increasing pressure on agriculture to meet the rising global food demand. In this context, the use of machine learning approaches to predict crop yields has gained significant importance. This paper aim is to study the significance of crop yield prediction through machine learning, its methods, applications, and its potential to revolutionize the agricultural sector. [ABSTRACT FROM AUTHOR]

Climate change-related impacts have hampered the productivity of agricultural lands in recent times, affecting food security globally. Novel technology-based agricultural production systems such as controlled-environment agriculture (CEA) are a way to reduce the impact of climatic variation and pests that harm current global crop production and ensure consistent crop development. These systems often use artificial lighting and soilless mediums to produce crops. This meta-analysis has investigated the key influencing factors on crop production within these systems, using previous studies on lettuce (the most cultivated crop in these systems) to understand what affects yield within CEA. This analysis has found that on average, CEA systems yield twice that of field-based agriculture (3.68 kg m−2 vs. 1.88 kg m−2), with the most influencing factors being the variety of cultivars grown, the season, the nutrient delivery method, and the lighting type. The cultivation time for this study was 40 days, with 94% of papers having trial periods of 70 days or less, much lower than field-based agriculture (60–120 days). Vertical farming (stacked vertical CEA cultivation) studies were found to especially drive up yield per area (6.88 kg m−2). The results of this meta-analysis are useful for starting to understand the key influencing factors on CEA growth and highlight the breadth of research ongoing in the CEA industry. [ABSTRACT FROM AUTHOR]

Trichoderma is a cosmopolitan fungus widely distributed around the world. The different uses of this beneficial fungus are varied in several industries, like agriculture, textile, and paper, including the pharmaceutical industry. The genus Trichoderma has other mechanisms of action, including the production of different enzymes and secreted secondary metabolites used in various industries. The genomes of different Trichoderma species have been sequenced to identify the mechanisms for producing several compounds. The advancement of multiple technologies has allowed the development of transformation tools for the genetic improvement of Trichoderma, thus increasing biomass, primary and secondary metabolites, and enzymes. Therefore, genetic modification aims to increase compound production in several Trichoderma strains. Characterization of Trichoderma through gene expression analysis is essential for biotechnology applications. It helps counteract one of the most challenging problems for agriculture today, including climate change and the appearance of pathogens that attack crops with high commercial and food demand. In conclusion, this review analyzes various strategies to improve Trichoderma strains genetically and their multiple applications in the agricultural, textile, paper, and pharmaceutical industries. As a recommendation for future studies with potential impact, the optimization of specific genetic modifications in Trichoderma strains is recommended to improve their adaptability and effectiveness in combating emerging challenges in agriculture, especially those linked to climate change. Investigating possible synergies between genetically modified Trichoderma strains and environmentally sustainable agricultural practices could contribute to developing solutions for crop protection and yield improvement. [ABSTRACT FROM AUTHOR]

Agricultural product costs play a significant part in the horticultural market. In India, vegetables, for example, tomatoes have the biggest supply and price variances among farming items. As tomatoes are grown around the year, outdoor and indoor, their yields change because of various factors, it is hard to settle tomatoes' inventory and costs. Although the Government puts numerous efforts to balance out the supply and costs of vegetables, continuous meteorological changes have prompted unstable supply and price fluctuations of vegetables. Accordingly, the right anticipating of vegetable costs is a significant issue. To oblige these, in this paper, an attempt has been made to dissect the costs and yield of tomatoes in India by utilizing a Machine Learning approach. This will unquestionably help the farmers and the Government if the anticipated costs are getting higher in the forthcoming months, then appropriate strategies can be made to diminish the costs of tomatoes. [ABSTRACT FROM AUTHOR]

This editorial paper takes the form of a concise report and delves into a critical and intricate issue essential for the sustainability of agriculture. It centers on the intricate relationship between agri-cultural water resource management and agronomical practices, as well as their ability to adapt to the impacts of climate change while ensuring both the quantity and quality of crop yields. Specifically, this paper serves as a synopsis of how the far-reaching consequences of climate change for water resources impact agricultural production. It also highlights primary adaptation strategies for managing agricultural water resources, as drawn from the existing literature. Such strategies are designed to counteract the potentially adverse impacts of climate change on the rural sector. Fur-thermore, this brief report offers a valuable overview of the 17 selected papers featured in this Special Issue (SI) on Water, published by MDPI. These papers serve as exemplars of cutting-edge approaches to adaptability in water resource management and resilient crop production systems, as these fields attempt to thrive in an ever-changing environmental landscape. [ABSTRACT FROM AUTHOR]

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences. [ABSTRACT FROM AUTHOR]

Weeds are one of the biggest barriers to crop productivity in recent years. Many efforts are being made to enhance the quality of agricultural crops which is a challenging task for farmers. Weeds are also affecting the quality of the crops along with the productivity of the crops. Weeds are the reason for the lack of water in the crops, because they absorb the moisture of the land, due to which the crops do not get proper water. Weeds also create hindrance during harvesting of crops. When farmers harvest crops, often toxic weeds also get mixed, due to which farmers do not get fair price for their crops. To prevent weeds, farmers spray herbicides uniformly throughout the field without knowing what type of weed it is. Due to which there is a lot of damage to the crops. Spraying herbicides also affects the environment, so it is essential to have a thorough knowledge of weeds so that specific weeds can be controlled. The main objective of this paper is to improve the accuracy of weed detection using machine learning techniques. [ABSTRACT FROM AUTHOR]

Examines the effects of paper mill sludge on crop yields. Soil fertility; Post-harvest soil analysis; Nitrogen immobilization and mineralization.

In India, there are more than a hundred crops planted around the whole country. These crops are categorized for better understanding and visualization. The data for this research has been acquired from the Indian Government Repository. This paper predicts the yield of almost all kinds of crops that are planted in India. This script makes novel by the usage of simple parameters like State, district, season, area and the user can predict the yield of the crop in which year he or she wants to. The paper uses advanced regression techniques like Kernel Ridge, Lasso and ENet algorithms to predict the yield and uses the concept of Stacking Regression for enhancing the algorithms to give a better prediction. [ABSTRACT FROM AUTHOR]

Effects of Bacillus thuringiensis (Bt) cotton are at the forefront of an intense debate on the benefits of genetically modified (GM) crops among smallholder farmers in developing countries. Existing studies fail to control for confounders, selection bias, or cultivation bias from preferential treatment in the initial adoption phase. Addressing these concerns in this paper, I examine the impact of Bt cotton employing an unbalanced panel fixed-effects model of a crop yield and profit function on newly collected plot-level data in the most recent decade. Results show that Bt cotton yields have stagnated, have a null effect on profits, and have become more sensitive to pest pressure in the most recent decade. Though many studies have demonstrated higher crop yield and profit gains in the first decade of Bt cotton adoption that raised the average returns to the technology, the second decade shows convergence in benefits, which raises obvious questions about the prospect of GM technology. Since Bt cotton is the only GM crop technology widely adopted by smallholder farmers, the findings of this paper contribute to the broader public debate on the future of agricultural biotechnology. [ABSTRACT FROM AUTHOR]

In order to obtain a good yield and high profitability for the winter wheat crop, a good interaction between the plant and the environment is needed for carrying out a strategic planning of the best agricultural practices. The most representative ones aim at the choice of varieties with genetic characteristics and high production capacity, the application of an optimal level of fertilization and the integrated fight against pathogens, which are key factors that contribute to increasing the productivity and quality of wheat harvests. The paper presents the analysis of the yield and profitability of the wheat crop in three large crop farms of different sizes in Romania. From the analysis carried out, in order to reach the profitability threshold, the following results were highlighted: on the farm of 60 ha, in the year 2021, on a cultivated area of 35.5 ha, the total production realized and sold must be 214.41 tons; on the farm of 600 ha, the cultivated area being 86 ha, the total production obtained and sold must be 514.24 tons; on the 3,000 ha farm, which had a cultivated area of 709.62 ha, the total cultivated and sold production must be 4,209.48 tons. It is necessary to specify the fact that in 2019 and 2020 the profitability threshold could not be reached, in most cases. This situation was due to drought, high production costs and low farm gate prices. The conclusion of the study is that the profitability of the winter wheat crop is difficult to be increased, taking into account the high and growing production expenses registered annually by farmers. In this situation, the increase in farm dependence on subsidies to ensure a favourable raw product for the winter wheat crop has clearly emerged. [ABSTRACT FROM AUTHOR]