Search Results (71)

The worldwide agriculture industry is facing increasing problems due to rapid population increase and increasingly unfavorable weather patterns. In order to reach the projected food production targets, which are essential for guaranteeing global food security, innovative and sustainable agricultural methods must be adopted. Conventional approaches, including traditional breeding procedures, often cannot handle the complex and simultaneous effects of biotic pressures such as pest infestations, disease attacks, and nutritional imbalances, as well as abiotic stresses including heat, salt, drought, and heavy metal toxicity. Applying phytohormonal approaches, particularly those involving hormonal crosstalk, presents a viable way to increase crop resilience in this context. Abscisic acid (ABA), gibberellins (GAs), auxin, cytokinins, salicylic acid (SA), jasmonic acid (JA), ethylene, and GA are among the plant hormones that control plant stress responses. In order to precisely respond to a range of environmental stimuli, these hormones allow plants to control gene expression, signal transduction, and physiological adaptation through intricate networks of antagonistic and constructive interactions. This review focuses on how the principal hormonal signaling pathways (in particular, ABA-ET, ABA-JA, JA-SA, and ABA-auxin) intricately interact and how they affect the plant stress response. For example, ABA-driven drought tolerance controls immunological responses and stomatal behavior through antagonistic interactions with ET and SA, while using SnRK2 kinases to activate genes that react to stress. Similarly, the transcription factor MYC2 is an essential node in ABA–JA crosstalk and mediates the integration of defense and drought signals. Plants’ complex hormonal crosstalk networks are an example of a precisely calibrated regulatory system that strikes a balance between growth and abiotic stress adaptation. ABA, JA, SA, ethylene, auxin, cytokinin, GA, and BR are examples of central nodes that interact dynamically and context-specifically to modify signal transduction, rewire gene expression, and change physiological outcomes. To engineer stress-resilient crops in the face of shifting environmental challenges, a systems-level view of these pathways is provided by a combination of enrichment analyses and STRING-based interaction mapping. These hormonal interactions are directly related to the United Nations Sustainable Development Goals (SDGs), particularly SDGs 2 (Zero Hunger), 12 (Responsible Consumption and Production), and 13 (Climate Action). This review emphasizes the potential of biotechnologies to use hormone signaling to improve agricultural performance and sustainability by uncovering the molecular foundations of hormonal crosstalk. Increasing our understanding of these pathways presents a strategic opportunity to increase crop resilience, reduce environmental degradation, and secure food systems in the face of increasing climate unpredictability. Full article

Sustainable Development Reserves are organized units in the Amazon that are essential for the proper use and sustainable management of the region’s natural resources and for the livelihoods and economy of the local communities. This study aims to provide a climatic characterization of the Madeira River Sustainable Development Reserve (MSDR), offering scientific support to efforts to assess the feasibility of implementing adaptation measures to increase the resilience of isolated Amazon communities in the face of extreme climate events. Significant statistical analyses based on time series of observational and reanalysis climate data were employed to obtain a detailed diagnosis of local climate variability. The results show that monthly mean two-meter temperatures vary from 26.5 °C in February, the coolest month, to 28 °C in August, the warmest month. Monthly precipitation averages approximately 250 mm during the rainy season, from December until May. July and August are the driest months, August and September are the warmest months, and September and October are the months with the lowest river level. Cold spells were identified in July, and warm spells were identified between July and September, making this period critical for public health. Heavy precipitation events detected by the R80, Rx1day, and Rx5days indices show an increasing trend in frequency and intensity in recent years. The analyses indicated that the MSDR has no potential for wind-energy generation; however, photovoltaic energy production is viable throughout the year. Regarding the two major commercial crops and their resilience to thermal stress, the region presents suitable conditions for açaí palm cultivation, but Brazil nut production may be adversely affected by extreme drought and heat events. The results of this study may support research on adaptation strategies that includethe preservation of local traditions and natural resources to ensure sustainable development. Full article

The agricultural sector faces significant sustainability, productivity, and environmental impact challenges. In this context, geographic information systems (GISs) have become a key tool to optimize resource management and make informed decisions based on spatial data. These data support planning the best cotton planting and harvest dates based on agroclimatic conditions, such as temperature, precipitation, and soil type, as well as identifying areas with a lower risk of water or thermal stress. As a result, cotton productivity is optimized, and costs associated with supplementary irrigation or losses due to adverse conditions are reduced. However, data from automatic weather stations in Mexico are scarce and incomplete. Instead, grid meteorological databases (DMM, in Spanish) were used with daily temperature and precipitation data from 1983 to 2020 to determine the heat units (HUs) for each cotton crop development stage; daily and accumulated HU; minimum, mean, and maximum temperatures; and mean annual precipitation. This information was used to determine areas that comply with environmental, geographic, and regulatory conditions (NOM-059-SEMARNAT-2010, NOM-026-SAG/FITO-2014) to delimit areas with agricultural potential for planting genetically modified (GM) cotton. The methodology made it possible to produce thirty-four maps at a 1:250,000 scale and a digital GIS with 95% accuracy. These maps indicate whether a given agricultural parcel is optimal for cultivating GM cotton. Full article

From the perspective of biogas plant (BGP) operators, it is highly challenging to make a profitable decision on optimal biomethane production and allocation across interconnected markets. The aim of this study is to analyze the dynamics of biomethane markets, develop the gas allocation portfolio (GAP) for BGPs, investigate the impact of GHG quota price on the market dynamics and substrate mix consumption, and evaluate the profitability of the biomethane market system under various demand-based scenarios. A two-step optimization approach based on linear programming is adopted. Firstly, the optimized substrate mix and corresponding GAP are determined for all BGPs. Secondly, by leveraging the options flexibility created by the interconnected nature of biomethane markets, the BGPs’ GAP is further developed. Through an in-depth sensitivity analysis, the effects of GHG quota price variations on the market dynamics are assessed. The results indicate that integrated production, obtained by implementing the improved GAP across all BGPs, maximizes the profitability of the system. At higher quota prices, the consumption of manure, residuals, and grass is encouraged, while the use of energy crops declines. Furthermore, higher quota prices lead to a substantial increase in biomethane price in the EEG market, highlighting the need for further governmental support for biomethane CHP units. The anticipated competition between hydrogen and biomethane to achieve a greater share in the heating sector could pose risks to long-term investments in biomethane. The system achieves its highest profitability, a total contribution margin of EUR 2254.8 million, under the Transport Biofuels Expansion scenario. Generally, policies and regulations that raise the quota price (e.g., the 36. BImSchV) or promote biomethane demand in the heating sector (e.g., the GEG) can provide both economic and ecological benefits to the system. Full article

The implementation of renewable energy systems (RESs) in the agricultural sector has significant potential to mitigate the negative effects of fossil fuel-based products on the global climate, reduce operational costs, and enhance crop production. However, the intermittent nature of RESs poses a major challenge to realizing these benefits. To address this, thermal energy storage (TES) and hybrid heat pump (HHP) systems are integrated with RESs to balance the mismatch between thermal energy production and demand. In pursuit of clean energy solutions in the agricultural sector, a 3942 m² greenhouse in Yeoju-si, South Korea, is equipped with 231 solar thermal (ST) collectors, 117 photovoltaic thermal (PVT) collectors, four HHPs, two ground-source heat pumps (GSHPs), a 28,500 m³ borehole TES (BTES) unit, a 1040 m³ tank TES (TTES) unit, and three short-term TES units with capacities of 150 m³, 30 m³, and 30 m³. This study evaluates the long-term performance of the integrated hybrid renewable energy and thermal energy storage systems (HRETESSs) in meeting the greenhouse’s heating and cooling demands. Results indicate that the annual system performance efficiencies range from 25.3% to 68.5% for ST collectors and 31.9% to 72.2% for PVT collectors. The coefficient of performance (COP) during the heating season is 3.3 for GSHPs, 2.5 for HHPs using BTES as a source, and 3.6 for HHPs using TTES as a source. During the cooling season, the COP ranges from 5.3 to 5.7 for GSHPs and 1.84 to 2.83 for ASHPs. Notably, the HRETESS supplied 3.4% of its total heating energy directly from solar energy, 89.3% indirectly via heat pump utilization, and 7.3% is provided by auxiliary heating. This study provides valuable insights into the integration of HRETESSs to maximize greenhouse energy efficiency and supports the development of sustainable agricultural energy solutions, contributing to reduced greenhouse gas emissions and operational costs. Full article

An important issue in the sustainable development of agricultural engineering today is the use of biogas plants for the production of electricity and heat from the organic waste of agricultural products and other low-quality products, which also contributes to the improvement of environmental safety. Traditional methods for assessing the apparent severity of the Roslynnytsia campaign based on statistics from the dominions proved to be ineffective. A hypothesis was proposed regarding the possibility of estimating the apparent biomass by averaging the indicators of depletion and assessing the CH₄ and CO emissions based on satellite monitoring data. The aim of this work is to create a methodology for preparing a raw material base in united territorial communities to provide them with electrical and thermal energy using biogas plants. The achievement of this goal was based on solving the following tasks: monitoring biomethane emissions in the atmosphere as a result of rotting organic waste, and monitoring carbon monoxide emissions as a result of burning agricultural waste. Experimental studies were conducted using earth satellites on sites with geometric centers in the village of Gaishin in the Pereyaslav united territorial community, the city of Ovruch in the Zhytomyr region, the Oleshkovsky Sands National Park in the Kherson region (Ukraine), and the city of Jüterbog, which is located in the state of Brandenburg and is part of the Teltow-Fläming district (Germany). The most significant results of this research involve the methodology for the preparation of the raw material base in the united territorial communities for the production of biogas, based on indirect measurements of methane and carbon dioxide emissions using the process of remote sensing. Based on the use of the proposed scientific and methodological apparatus, it was found that the location of the territory with the center in the village of Gaishin has better prospects for collecting plant raw materials for biogas production than the location of the territorial district with the center in the city of Ovruch, the emissions in which are significantly lower. From March 2020–August 2023, a higher CO concentration was recorded on average by 0.0009 mol/m², which is explained precisely by crop growing practices. In addition, as a result of the conducted studies, for the considered emissions of methane and carbon monoxide for monitoring promising raw materials, carbon monoxide has the best prospects, since methane emissions can also be caused by anthropogenic factors. Thus, in the desert (Oleshkivskie Pisky), large methane emissions were recorded throughout the year which could not be explained by crop growing practices or the livestock industry. Full article

Agriculture is an important part of the economy of southwestern United States (Southwest). The production of food and fiber in the Southwest is supported by irrigation, much of which is sourced from groundwater. Climate projections suggest an increasing risk of drought and heat, which can affect water supply and demand, and will challenge the future of agricultural production in the Southwest. Also, as groundwater in the Southwest is highly regulated, producers may not be able to readily rely on groundwater to meet increased demand. Climate exposure of five economically-important crops—alfalfa, cotton, pecans, pistachios, and processing tomatoes—was analyzed over twelve regulated groundwater basins by quantifying changes in a suite of both crop-specific and non-specific agroclimatic indicators between contemporary (1981–2020) and future (2045–2074, SSP2-4.5) climates. Generally, groundwater basins that are currently the most exposed to impactful climate conditions remain so under future climate. The crops with the greatest increase in exposure to their respective crop-specific indicators are cotton, which may be impacted by a ~180% increase in exposure to extreme heat days above 38 °C, and processing tomatoes, which may see a ~158% increase in exposure to high temperatures and reduced diurnal temperature range during flowering. These results improve understanding of the potential change in exposure to agroclimatic indicators, including crop-specific indicators, at the scale of regulated groundwater basins. This understanding provides useful information for the long-term implications of climate change on agriculture and agricultural water, and can inform adaptation efforts for coupled agricultural and water security in groundwater-dependent regions. These results may also be useful for assessing the adaptive potential of water conservation actions—some of which are outlined herein—or the suitability of other adaptation responses to the challenges that climate change will pose to agriculture. Full article

Soybean is the primary oilseed crop in the United States, with significant industrial value. Understanding the molecular mechanisms of heat stress tolerance in soybean plants is critical for developing stress-resistant cultivars. Current knowledge about the role of fatty acid desaturases (FADs) in modulating membrane fluidity under abiotic stress prompted this investigation into the impact of mutations in the FAD genes on seed germination from heat-stressed plants. In soybean plants, exposure to heat stress during anthesis is known to significantly reduce seed germination. In silico expression analysis indicated high expression levels of the soybean FAD2 and FAD3 genes in the leaves. Therefore, a detailed expression analysis of these genes was conducted using qRT-PCR from leaf tissue. Generally, downregulation of these genes was observed in the mutants; however, two genes, FAD3A and FAD2-3, showed a more than 2-fold increase in expression in six out of ten mutants under heat stress. This upregulation was particularly pronounced (7-fold) in the mutant S17CR-170. Correlation analysis revealed a positive correlation (up to 0.48) between the expression level of FAD3A, FAD3B, FAD3C, and FAD2-3 and the decline in germination from heat-stressed plants. This suggests these FAD genes may act as negative regulators of germination under heat stress conditions. Full article

To face climate change, searching for alternative crops resistant to drought and heat stress becomes necessary, along with efficient germplasm management. Old landraces well-adapted to local climatic conditions, pests, and pathogens could be used as a source of desired traits. Cowpea (Vigna unguiculata L. Walp.), grown mainly in the tropical and subtropical areas, presents superior drought and heat stress adaptation compared to other legumes. Limited information is available on field performance and nutrient qualities of cowpea landraces originating from southern Bulgaria. The aim of the present study was to compare in field conditions and their impacts on plant performance, yield, and the seed biochemical composition of Bulgarian cowpea accessions, including fourteen landraces and one variety. Higher-yielding, earlier-maturing accessions were discerned. Among the landraces studied, B1E0103 was the most productive under the agro-climatic conditions in Sadovo, central Bulgaria; BOE0035 had the earliest maturity. The seed moisture content was 11.5 ± 0.3%, and the energy value was 347.9 ± 1.2 kcal/100 g. The crude protein content varied from 22.5 to 27%, the lipids were 1.6–2.55%, the carbohydrates were 56.5–61.4%, ash was 3.8–4.3%, dietary fibers were 3.1–4.5%, tannins were 16–22%, phenols were 1.3–4.4 mg/g, flavonoids were 1.85–3.7 mg/g, and the trypsin-inhibiting activity was 0.7–2.5 units/mg FW, with the lowest in BOE0010, the variety “Hrisi”, and B1E0103 and the highest in B0E0035, A9E1230, and A8E0562. Landraces are promising genetic material for future research and breeding purposes. Full article

Drought stress has a significant impact on agricultural productivity, affecting key crops such as soybeans, the second most widely cultivated crop in the United States. Endophytic and rhizospheric microbial diversity analyses were conducted with soybean plants cultivated during the 2023 growing season amid extreme weather conditions of prolonged high temperatures and drought in Louisiana. Specifically, surviving and non-surviving soybean plants were collected from two plots of a Louisiana soybean field severely damaged by extreme heat and drought conditions in 2023. Although no significant difference was observed between surviving and non-surviving plants in microbial diversity of the rhizosphere, obvious differences were found in the structure of the endophytic microbial community in root tissues between the two plant conditions. In particular, the bacterial genera belonging to Proteobacteria, Pseudomonas and Pantoea, were predominant in the surviving root tissues, while the bacterial genus Streptomyces was conspicuously dominant in the non-surviving (dead) root tissues. Co-occurrence patterns and network centrality analyses enabled us to discern the intricate characteristics of operational taxonomic units (OTUs) within endophytic and rhizospheric networks. Additionally, we isolated and identified bacterial strains that enhanced soybean tolerance to drought stresses, which were sourced from soybean plants under a drought field condition. The 16S rDNA sequence analysis revealed that the beneficial bacterial strains belong to the genera Acinetobacter, Pseudomonas, Enterobacter, and Stenotrophomonas. Specific bacterial strains, particularly those identified as Acinetobacter pittii and Pseudomonas sp., significantly enhanced plant growth metrics and reduced drought stress indices in soybean plants through seed treatment. Overall, this study advances our understanding of the soybean-associated microbiome structure under drought stress, paving the way for future research to develop innovative strategies and biological tools for enhancing soybean resilience to drought. Full article

Understanding the causes of spatiotemporal variation in crop yields across large areas is important in closing yield gaps and producing more food for the growing global population. While there has been much focus on using data-driven models to predict crop yield, there is also an opportunity to use these empirical models to understand which factors are driving variations in yield and to quantify their contributions. This study uses a large database of 625 rainfed wheat yield maps from 14 different seasons (2007–2020) across the eastern grain belt of Australia. XGBoost models were used, with predictors including maps of soil attributes (e.g., pH and sodicity), along with weather indices (rainfall, frost, heat, growing degree days). The model and predictors could accurately predict field-scale yield, with a Lin’s concordance correlation coefficient (LCCC) of 0.78 with 10-fold cross-validation. SHapley Additive exPlanation (SHAP), a form of interpretive machine learning (IML), values were then used to assess the impact of the variables on yield. The SHAP values for each predictor were also mapped onto a grid of the study area for the 2020 season, which showed the impact of each predictor on wheat yield (t ha⁻¹) and revenue (AUD ($) ha⁻¹) in interpretable units. Weather variables, such as rainfall and heat events, had the largest impact on yield. Although generally less significant, soil constraints such as soil sodicity were still important in driving yield. The results also showed that despite their largely temporally stable nature, soil constraints impact yield differently, depending on seasonal conditions. Overall, data-driven models and IML proved valuable in understanding the impact of important weather and soil variables on wheat yield and revenue across the eastern Australian grain belt. This could be used to determine the magnitude and economic impact of soil constraints and extreme weather on crops across regions and to inform policies and farm management decisions. Full article

Several years have passed since the linear economy model proved unsustainable, leading to the transition toward the circular economy (CE) model. Significant amounts of agricultural residues and waste from livestock farming units remain unutilized in fields. The anaerobic digestion (AD) method addresses this issue by generating energy in the form of thermal (TE) and electrical energy (EE). This article examines greenhouse heating using thermal energy from a biogas plant. For this purpose, a thermal load model is developed and applied in two regions, northern (Florina) and central Greece (Trikala), to assess the greenhouse’s energy requirements in areas with differing characteristics, especially during the winter months. Additionally, the economic benefits of a biogas plant from selling electricity to the grid are analyzed. Thermal energy constitutes 59.7% of the system’s total energy output. On average, the generated electrical energy amounts to 518 MW h per month, while thermal energy reaches 770 MW h per month. The biogas plant’s daily electricity consumption ranges from 1564 kW h to 2173 kW h, depending on its needs. Ambient temperatures vary between 0 °C and 37 °C, significantly influencing the greenhouse heating system’s efficiency. The biogas plant also demonstrates financial profitability, earning 504,549 € annually from the sale of surplus electricity. Furthermore, the article explores greenhouse crops in the broader Thessaly region, where tomato cultivation seems to be dominant. Greenhouse heating requirements depend on crop type, location, weather conditions, sunlight exposure, and heat loss based on covering materials. Meanwhile, the thermal energy output that can heat a given greenhouse area is directly proportional to the biogas plant’s capacity. Full article

Incorporating data-driven technologies into agriculture presents a promising approach to optimizing crop production, especially in regions dependent on irrigation, where escalating heat waves and droughts driven by climate change pose increasing challenges. Recent advancements in sensor technology have introduced diverse methods for assessing irrigation needs, including meteorological sensors for calculating reference evapotranspiration, belowground sensors for measuring plant available water, and plant sensors for direct water status measurements. Among these, infrared thermometry stands out as a non-destructive remote sensing method for monitoring transpiration, with significant potential for integration into drone- or satellite-based models. This study applies infrared thermometry to develop a crop water stress index (CWSI) model for European hazelnuts (Corylus avellana), a key crop in Oregon, the leading hazelnut-producing state in the United States. Utilizing low-cost, open-source infrared thermometers and data loggers, we aim to provide hazelnut farmers with a practical tool for improving irrigation efficiency and enhancing yields. The CWSI model was validated against plant water status metrics such as stem water potential and gas exchange measurements. Our results show that when stem water potential is below −6 bar, the CWSI remains under 0.2, indicating low plant stress, with corresponding leaf conductance rates ranging between 0.1 and 0.4 mol m² s⁻¹. Additionally, un-irrigated hazelnuts were stressed (CWSI > 0.2) from mid-July through the end of the season, while irrigated plants remained unstressed. The findings suggest that farmers can adopt a leaf conductance threshold of 0.2 mol m² s⁻¹ or a water potential threshold of −6 bar for irrigation management. This research introduces a new CWSI model for hazelnuts and highlights the potential of low-cost technology to improve agricultural monitoring and decision-making. Full article

The extreme heat and water scarcity of the desert southwest in the United States of America present significant challenges for growing food crops. However, controlled-environment agriculture offers a promising solution for plant production in these harsh conditions. Glasshouses and plant factories represent advanced but energy-intensive production methods among controlled-environment agriculture techniques. This review aims to comprehensively assess how controlled-environment agriculture can thrive and be sustained in the desert southwest by evaluating the energy efficiency of controlled glasshouses and building-integrated plant factories. The analysis focuses on the efficiency of these systems’ energy and water consumption, mainly using artificial lighting, heating, cooling, ventilation, and water management through various hydroponic techniques. Approximately 50% of operational energy costs in controlled glasshouses are dedicated to cooling, whereas 25–30% of energy expenses in building-integrated plant factories are allocated to artificial lighting. Building-integrated plant factories with aeroponic systems have demonstrated superior water use and energy efficiency compared to controlled glasshouses in desert environments. Integrating photovoltaic solar energy and glass rooftops in building-integrated plant factories can significantly reduce energy costs for urban farming in the desert southwest. Full article

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices. Full article