Robert J. Harrison, John G. Howieson, Ron J. Yates
et al.
Abstract Physical dormancy in seeds is caused by a water-impermeable seed coat. It plays a crucial role in the sustainability of ley farming systems in Mediterranean climates by enabling pasture legumes to regenerate from a soil seed bank. Sophisticated management of the pattern of physical dormancy break can synchronize germination of plants with the optimal growing season, essential to the sustainability of the system. The ability of the maternal plant to reproduce and provide its progeny (seed) with sufficient resources is influenced by both genetic and environmental factors. However, when abiotic (e.g., drought, temperature) or biotic (e.g., pests, diseases) stressors affect the maternal plant during its reproductive phase, the allocation of resources to seeds can vary, leading to variability in physical dormancy outcomes. This genetic × environment interaction is well documented; however, a third factor, management, is less understood. Herein lies a significant knowledge gap, and this is the first review that examines physical dormancy from an agricultural management perspective. In the manuscript, we explore the evolution of physical dormancy in pasture legumes in ley farming systems and how the intensification of agricultural management practices inadvertently affects physical dormancy development and the pattern of release. This intensification threatens the sustainability of ley farming by changing the timing of physical dormancy release, which concomitantly occurs under changed climatic conditions. Therefore, climate change combined with the intensification of agricultural management practices, including pesticide use, altered grazing regimes, and frequency of crop rotation, poses significant challenges to physical dormancy regulation and soil seed bank dynamics in ley farming. We suggest that to combat the impacts of agricultural intensification, detailed studies and breeding programs should focus on selecting legumes with both agricultural and climatic resilience for successful adaptation to evolving agricultural landscapes and ensure continued sustainable productivity.
Conventional water resource carrying capacity (WRCC) assessment methods often struggle to capture dynamic water-economy-ecosystem interactions and lack standardized, reproducible frameworks. This study introduces water accounting—particularly the System of Environmental-Economic Accounting for Water (SEEA-W)—as a comprehensive methodological upgrade for WRCC assessment. Through a systematic review of existing methods and a detailed case study, we demonstrate how SEEA-W integrates hydrological processes with socioeconomic data using a structured supply-use framework, enabling multi-scale, policy-relevant, and comparable assessments. Our analysis identifies five critical dimensions for enhancing WRCC practices: spatiotemporal scalability, standardized account compilation, data gap mitigation, integration with ecosystem accounting (SEEA-EA), and modular indicator development. The results highlight the potential of SEEA-W in identifying water-use conflicts, evaluating efficiency, and supporting sustainable water governance. Finally, we outline key challenges—such as data consistency and regional adaptability—and propose strategies for future advancement, including the adoption of hybrid data integration techniques and unconventional water resource accounting. This research provides a new pathway toward more systematic and adaptive water resources management.
Climate variability, particularly freeze stress, poses a substantial challenge to crop yields worldwide. This study examined the impact of early 2024 freeze stress on rapeseed yields in the Yangtze River Basin, China, and assessed yield responses to nitrogen (N), phosphorus (P), and potassium (K) fertilizer rates. Six field experiments with varying N, P, and K fertilizer rates were conducted from 2022 to 2024 at two sites. In 2023–2024, a severe freeze event caused yield losses ranging from 13.4% to 63.3%, depending on nutrient fertilizer rates and sites. The effect of N fertilization on mitigating freeze stress varied across different sites, while high P fertilizer rates were associated with a reduced yield decline under freeze stress. The K fertilizer application also decreased the yield reductions caused by the freeze stress. Freeze stress disproportionately affected yield components, particularly the number of siliques per plant. Membership function values (MFV) were used as a comprehensive indicator of yield-related traits to quantify the combined effects of freeze stress and fertilization on rapeseed yield. The optimal fertilizer rates that maximized MFV were 343 kg N ha−1, 118 kg P2O5 ha−1, and 166 kg K2O ha−1 for 2022–2023 and 239 kg N ha−1, 110 kg P2O5 ha−1, and 169 kg K2O ha−1 for 2023–2024. These results highlight the importance of balanced nutrient management in improving rapeseed resilience to freeze stress and provide practical recommendations for optimizing nutrient management in cold-prone regions.
Mohammad Saleh, Mobin Salehi, Shayan Khanaki
et al.
The global agricultural sector, as the largest consumer of water, faces critical challenges related to freshwater scarcity and quality. Treated wastewater (TWW) irrigation presents a viable solution, prompting this study to examine its effects on soil and crops over a two-year experiment (2020–2021) in Hashtgerd and Mahdasht, Karaj, Iran, across different growing seasons. Wheat, barley, alfalfa, and maize were cultivated in two farms per region. Findings revealed significant impacts on electrical conductivity (EC), nutrient levels (nitrogen and phosphorus), and heavy metal dynamics, varying based on TWW quality and soil properties. Notably, soil EC increased by 1.08 and 1.38 dS/m in Hashtgerd farms, while Mahdasht saw rises of 3.36 and 3.20 dS/m, reflecting regional disparities in TWW composition. Nitrate concentrations in Mahdasht increased by up to 25 mg/kg compared to baseline levels, while lead accumulation remained below critical thresholds in both regions. These region-specific values reflect local variations in water quality and soil characteristics, contributing to a broader understanding of spatial differences in TWW irrigation outcomes. While TWW enriched the soil with nutrients like nitrogen and phosphorus, it also posed risks such as salinization, nitrate leaching, and heavy metal accumulation, especially in Mahdasht, where wastewater quality is lower. Crop productivity improved for maize and barley under TWW irrigation, but wheat and alfalfa showed inconsistent outcomes, including occasional yield declines and nutrient imbalances. Although heavy metals in crops remained mostly within safe limits, nickel and lead exhibited worrisome trends. These findings emphasize the dual role of TWW in improving soil fertility and crop productivity while presenting environmental and health challenges.
Mahmoud Y. Shams, Wael M. Elmessery, Awad Ali Tayoush Oraiath
et al.
Broiler weighing is essential in poultry production for growth monitoring, feed management, health detection, and meeting market requirements. Traditional weighing methods, which use electronic platform weighers, can stress broilers and may not capture accurate weight data, particularly heavy broilers. To overcome these limitations, this study proposes a camera-based weighing approach that relies on morphological changes in different growth stages of broilers rather than body dimensions. The study utilizes YOLO version 8, a deep learning-based network segmentation technique, for precise broiler segmentation, significantly improving weight accuracy in complex environments. The YOLOv8 architecture builds a model that demonstrates improved and trustworthy results in broiler weight prediction, achieving a mean average precision across a range of intersection over union thresholds from 50 % to 95 % of 0.829. By accurately estimating broiler weights based on their morphological features, the developed trained YOLOv8 model eliminates the need for measuring their dimensions or sizes, making the process efficient and convenient.
Indonesia is the world’s largest coconut producer, with Indragiri Hilir being one of its major coconut-producing regencies. Despite the extensive coconut plantations in the region, the development of the coconut agroindustry remains underdeveloped. Coconut oil, a high-demand product in international markets, represents a significant opportunity for agroindustrial growth. Effective agroindustrial development requires accurate determination of raw material needs to plan production capacity and ensure efficient operation. Forecasting demand is a critical first step in this process, as it can enhance efficiency, reduce unnecessary raw material stockpiling, and minimise waste. This study aimed to forecast the demand for coconut oil, which is a crucial step for determining raw material requirements in the development of the coconut oil agroindustry. The research integrated ARIMA and mass balance methods. The findings indicated that coconut oil demand was projected to reach 556,749 tons per year from 2025 to 2030, with a corresponding raw material requirement of 4,259,130 tons per year. This study provides valuable insights for both farmer and agroindustry stakeholders in advancing the development of the coconut oil agroindustry.
Sota Takagi, Yusuke Numazawa, Kentaro Katsube
et al.
Abstract In the context of the urgent need to establish sustainable food systems, community-supported agriculture (CSA), in which consumers share risks with producers, has gained increasing attention. Understanding the factors that influence consumer participation in CSA is crucial, yet the complete picture and interrelations of these factors remain unclear in existing studies. This research adopts a scoping review and the KJ method to elucidate the factors influencing consumer participation in CSA and to theorize consumer participation. In particular, we focus on the dynamics of individual decision-making for participation, under the premise that individuals are embedded in socio-cultural environments. We examine the decision-making process based on the seesaw of expected gains and losses from participation, along with the reflexivity to the individual and the process of updating decision-making post-participation. Our study highlights how individual decision-making for participation is influenced by relationships with others within the embedded socio-cultural environment, as well as by attachment and connection to the community. It also shows that discrepancies between expectations and experiences post-participation, and the transformation of the social capital, promote the updating of decision-making processes. In addition, among the factors identified in this study for participation in CSA, the decision to participate was heavily influenced by expectations of “variety of ingredients,” suggesting that other factors such as “food education and learning opportunities,” “contribution to environmental and social issues,” and “connections with people and nature” had little impact. Although there are limitations, the insights gained from this study offer profound implications for stakeholders and provide valuable insights for more sustainable and efficient CSA practices.
Nutrition. Foods and food supply, Agricultural industries
Water, food, and energy are essential resources that require innovative and sustainable production methods. Researchers always try hard to create changes in food production methods and new energy sources. Recent advancements in ocean industry technologies, leveraging surface hot water and cold deep-sea water (DSW), offer promising solutions. While the ocean's water resources are vast, the specific physical and chemical characteristics of DSW are crucial to determining its potential industrial applications.Iran's southern coast, particularly in the southeast region, faces significant agricultural challenges due to inadequate soil quality and limited freshwater resources. This research investigates the properties of DSW in this region, assessing key physical and chemical parameters from selected sampling stations. The findings demonstrate the presence of DSW with suitable characteristics for industrial use. These results open the door to various applications, including ocean-based greenhouses, Ocean Thermal Energy Conversion (OTEC), aquaculture under controlled conditions, and the cultivation of salt-tolerant plants.The results indicate favorable conditions for establishing oceanic industries, including salt-tolerant plant cultivation, aquaculture farms, producing essential food products, tourism, water therapy, and renewable energy generation. Significant governmental investment in infrastructure and DSW extraction is required. Then, private enterprises invest in factories and facilities.
In the realm of commercial rabbit farming, assessment of liveweight for each rabbit is crucial for production management. Traditional manual weighing methods are labor-intensive, time-consuming, and can induce stress and disease transmission. To solve this problem, this paper introduced a liveweight estimation method for rabbit utilizing near-infrared and depth image, including a segmentation stage and a weight estimation stage. Specifically, a dual-stream feature fusion mechanism was proposed to effectively integrate information from both near-infrared and depth imaging. The network was trained and validated on a dataset of 1,957 overhead images from 300 rabbits collected from a commercial farm, achieving superior performance with an R-Square (R2) of 0.95, Root Mean Square Error (RMSE) of 194.95 g, Mean Absolute Deviation (MAD) of 155.10 g, Mean Absolute Percentage Error (MAPE) of 4.08 %, and Mean Coefficient of Variation (MCV) of 3.03 %. Ablation experiments revealed that employing the proposed dual-stream feature fusion mechanism led to reductions of 52.87 g, 38.19 g, 1.01 %, and 0.75 % in RMSE, MAD, MAPE, and MCV, respectively, compared to not employing the mechanism. Compared to machine learning method, our proposed approach demonstrates superior performance in RMSE, MAD, MAPE, and MCV, achieving more accurate liveweight estimation for large-scale commercial farms.
Abstract The Food and Agriculture Organization estimates that more than 800 million people engage in urban agriculture producing more than 15% of the world's food. Recently, there has been a resurgence of interest in urban agriculture in many wealthy, developed cities, with new technology and agro-architecture being employed to grow food in cities at commercial scale. This has been accompanied by an increase in media coverage. Big claims are being made, including that urban agriculture can decrease greenhouse emissions, ‘climate proof’ farms, help solve food security for growing urban populations and provide chemical free food with no risk of pests and diseases. Many of these claims need to be rigorously tested to ensure that sound investments can be made in enterprises that are financially viable and capable of delivering on claims of social and environmental benefits. Around the world, traditional broadacre and horticulture farming have been underpinned by years of biological, chemical, physical, economic and social research. Urban agriculture needs similar support as the industry grows and develops around the world. There are opportunities to improve crop yields and quality by pairing advancements in environmental controls, phenomics and automation with breeding efforts to adapt traits for architecture, development and quality (taste and nutrition) allowing a more diverse set of crops to be grown in controlled-environment farms. Urban farms are uniquely placed to take advantage of urban waste energy, water and nutrients but innovations are needed to use these resources safely and economically. This review discusses the technological research and innovations necessary for urban agriculture to meet the nutritional requirements of growing urban populations.
S. Fountas, Borja Espejo-García, A. Kasimati
et al.
This article presents key technological advances in the digital agriculture, which will have significant impact. Artificial intelligence-based techniques, together with big data analytics, address the challenges of agricultural production in terms of productivity and sustainability. Emerging new applications will transform agriculture from the traditional farm practices to a highly automated and data intensive industry.
The application of rainwater harvesting and mulching planting (RHMP) methods has effectively improved the productivity in dryland farming. However, different RHMP methods lead to differences in soil water availability, which must be matched with appropriate amounts of fertilizer to obtain further improvements in farmland productivity. In this study, we conducted a long-term (2014–2019) in situ experiment by using a split-plot design with four planting methods (control treatment, NN, no film mulching and no ridge; RHMP, HS, half-film mulching and single ridge; HN, half-film mulching and no ridge; FD, full-film mulching and double ridge) as main treatments and four fertilizer application levels (N, no fertilizer; L, low fertilizer; M, medium fertilizer; and H, high fertilizer, with N:P2O5 rates of 0:0, 150:75, 300:150, and 450:225 kg ha–1, respectively) as sub-treatments. We studied the long-term soil water dynamics, crop growth responses, and economic benefits under the interactive effects of RHMP and fertilization. The results showed that RHMP changed the pattern of soil water consumption and recovery, thereby making it more compatible with the water demand of spring maize, and improving the water availability. Compared with NN, HS, HN, and FD increased the evapotranspiration by 8.7, 12.5, and 20.7 mm, respectively, the yield by 19.8%, 21.1%, and 69.4%, and the water use efficiency by 17.5%, 17.8%, and 61.8%. Under the same RHMP method, fertilization increased the consumption of soil water by crops but improved the water use efficiency. Compared with N, L, M, and H increased the evapotranspiration by 13.1 mm, 14.1 mm, and 15.4 mm, respectively, the yield by 198.7%, 228.5%, and 243.7%, and the water use efficiency increased significantly by 190.0%, 218.2%, and 231.9%. Due to the high variability of inter-annual rainfall (differences up to 419.1 mm during 2014–2019), the soil water availability varied significantly in different years, thereby affecting the yield and economic benefits. We also analyzed the responses in terms of the grain yield and net income to fertilizer application, as well as the soil water storage in the 0–200 cm soil layer before sowing under different RHMP methods. The amount of fertilizer that obtains the best yield and maximum economic benefit in the current year can be evaluated according to the soil water conditions before sowing in order to maximize the production of spring maize in the current season. The results obtained in this study provide a simple method for determining the amount of fertilizer based on water availability to improve and stabilize the crop production efficiency in arid regions.
The agricultural industry in the Russian Federation is dynamically developing; the agricultural export amounts to $ 25 billion. In other countries, in turn, this figure is much higher, for example, in China it has exceeded $ 75 billion. The existing potential can be realized if the efficiency of agricultural production is increased by creating and implementing automation, robotization, digital technologies, and artificial intelligence. As a result it may lead to a 2.5-3.5-fold increase in labor productivity; a 2-3-fold increase in the yield of crops; a 3-4-fold cut in energy consumption and material costs, ensuring the ecological safety of agricultural production and the environment. The authors developed the concept of intelligent agriculture and identified the following areas of digital technology applications: integrated production management; digital technologies in crop production, animal husbandry, energy supply, products storage and processing ; digital engineering for rural areas. The authors presented the stages of agricultural production digitalization, including: a system for monitoring the conditions and parameters of agricultural production; information transmission system; artificial intelligence and cloud technologies, setting the foundation for management decision-making; the implementation of management decisions by robotic devices. The authors presented the examples of using digital technologies in soil cultivation, horticulture, animal husbandry, and artificial ecosystems. In animal husbandry, these technologies prove to facilitate the monitoring of the animals movement, their physiological state, parameters of the microclimate on the premises, feed and milk quality control. The Federal Scientific Agroengineering Center VIM is reported to have the necessary educational infrastructure, accredited Master’s and postgraduate studies for training specialists in digital agriculture.
Michele Lins Aracaty e Silva, Mauro Mauricio Barbosa Lucas, Leonardo Marcelo dos Reis Braule Pinto
O Estado do Amazonas apresenta os mais elevados indicadores de vulnerabilidade socioeconômica, incluindo a pobreza, fome, vulnerabilidade, desigualdade social, baixa taxa de prosperidade social, renda per capita insuficiente, insegurança alimentar, informalidade e dependência de programas de transferência de renda, situação agravada com a chegada do COVID-19 visto a fragilidade do sistema de saúde. Tal realidade, pode explicar a necessidade da população mais vulnerável em romper as barreiras do isolamento social e permanecer nas ruas elevando a taxa de contaminação. Assim, este artigo tem como objetivo analisar as vulnerabilidades socioeconômicas do Estado do Amazonas de forma a justificar a baixa adesão ao isolamento social e de permanência em casa. Para tanto, baseia-se em pesquisa bibliográfica e documental com o uso de dados secundários do IBGE, ONU e IPEA, analisando os indicadores do IVS, IDHM, pobreza, extrema pobreza, fome, renda per capita, Índice de Gini e Taxa de Prosperidade Social. Os resultados mostraram avanço no IVS e no IDHM ao longo dos anos, elevado percentual de Pobreza, Pobreza Extrema e Desigualdade Social, elevado percentual de Insegurança Alimentar, Renda Per Capita e Taxa de Prosperidade Social baixas, elevado percentual de informalidade (58,35%) e de dependência de programa de transferência de renda (25%). Os números da saúde agravados pelo COVID-19, apontam a insuficiência de leitos, respiradores e de profissionais de saúde, elevada taxa de reprodução da doença, elevado grau de mortalidade e de coeficiente de incidência, baixo índice de isolamento social, elevado número de mortes diárias, colapso no sistema funerário e em relação à 2ª onda a crise por escassez de oxigênio de uso medicinal.