Search Results (770)

This study assessed the vulnerability of canola, spring wheat, and barley yields in the Canadian Prairies to drought stress under future climate scenarios, integrating DSSAT crop models with NEX-GDDP CMIP6 projections and probabilistic copula analysis. The DSSAT simulations reproduced historical yields with high accuracy (d > 0.7, nRMSE < 15–20%), confirming its applicability for Prairie agroecosystems. Results indicate distinct crop-specific sensitivities to warming: barley showed relative resilience with modest yield gains (~10%) at 1.5–2 °C of global warming (GW), wheat exhibited heterogeneous responses with early minor gains (~1%) followed by declines (~8%) beyond 3 °C of GW, and canola displayed consistent and substantial losses (20–37%) even under moderate warming. Spatial analysis highlighted relatively stable regions in northern Alberta, central Saskatchewan, and southern Manitoba (Gray and Black soil zones), while the southern and southwestern Prairie areas (Brown and Black-Brown zones) showed the greatest yield declines. Copula-based analysis further revealed that canola is most vulnerable to dry conditions, with yield exceedance probabilities falling from 62% (wet years) to ~25–28% (dry years) under GW. These findings underscore that Prairie crop production faces increasingly heterogeneous risks, with canola emerging as the most climate-sensitive crop. Targeted adaptation strategies such as stress-tolerant cultivars, shifting cropping zones, and improved water management will be essential to mitigate projected drought impacts and sustain Prairie agricultural productivity. Full article

The Ural Mountains in the Western Siberia region cultivate over 3.5 M ha of short season spring wheat, with an average grain yield of 1.6–2.0 t/ha. The study focus was the analysis of climate change and weather effects on spring wheat yields from 2001 to 2024 and on genotype–environment interactions in the Kazakhstan–Siberia Spring Wheat Improvement network (KASIB) trials from 2019 to 2024. Climate change has the tendency to gradually reduce precipitation and increase air temperatures, which negatively affect spring wheat yields. Based on regional yield and weather, the region was divided into subregions: Tyumen in the North with a high yield; Chelyabinsk with lower precipitation and a lower grain yield; and Omsk and Kurgan were similar in most years. Environments at the four breeding programs (Chelyabinsk Agricultural Research Institute, Kurgan Seeds, and Omsk and Tyumen State Agrarian Universities) did not fully reflect the target production areas due to a very high yield gap and lack of association between the research and production yields. Genotype–environment interaction analysis showed that the Tyumen site had the highest yield and best discriminating ability, while Chelyabinsk best represented the whole target region. Most of the highest yielding material in KASIB trials originated from outside of the region. Spring wheat breeding programs in the region ought to improve to maintain a competitive edge. Full article

To clarify the optimal water regulation strategy for spring wheat in arid areas, this study set up three irrigation methods [film-mulched drip irrigation (FD), non-mulched drip irrigation (ND), non-mulched subsurface drip irrigation (MD)] and five water treatments [CK: 80% field capacity; W1–W4: irrigation amounts were 90%, 80%, 70%, and 60% of CK, respectively] in the Shiyang River Basin during 2023–2024. The effects of these treatments on the phenotype, yield, and water use efficiency (WUE) of spring wheat were investigated. The results showed that under the same water treatment, the leaf area index (LAI), SPAD value, and stem diameter (SD) significantly decreased with the reduction in irrigation amount (p < 0.05), while plant height (HC) was less affected. FD performed optimally under the W1 treatment: its yield reached 11,868.93 kg·ha⁻¹, which was 54.88% and 38.72% higher than that of ND and MD, respectively; and its WUE reached 4.36 kg/m³, which was 123.19% and 100.83% higher than that of ND and MD, respectively. ND performed better under the CK treatment: its yield was 10,044.33 kg·ha⁻¹, which was 27.07% and 12.25% higher than that of FD and MD, respectively. Annual precipitation had a significant impact: when precipitation was 175 mm in 2023, ND showed an obvious advantage; when precipitation decreased to 110 mm in 2024, FD exhibited stronger stress resistance. The study concludes that FD is suitable for moderate to severe water stress, while ND is suitable for sufficient water conditions or mild stress. This can provide a basis for water-saving and the high-yield production of spring wheat in arid areas. Full article

Ammonia poses a risk to human health and terrestrial and aquatic ecosystems. In Spain in 2022, the agricultural sector was responsible for 97% of ammonia emissions to the atmosphere, with the application of animal manure as fertilizer accounting for 24.4% of these emissions. The search for effective mitigation strategies in the application of animal manures is imperative to support the implementation of policies that contribute to the sustainability of the agricultural sector. The aim of this study is to evaluate three digestate application techniques, namely, acidification, open disc injection, and fertigation, in a wheat–maize rotation and compare them to traditional trail hose application. In spring wheat topdressing, acidification is the most efficient method for reducing ammonia emissions, followed by disc injection and, finally, fertigation. In the summer base dressing to maize, acidification is the best method, with more than 70% reduction compared with trail hoses. In terms of both base dressing and side-dressing fertilization, the most efficient method is fertigation, with a 70% reduction, followed by acidification and disc injection (>25%). Although the three methods reduce ammonia emissions, they have certain drawbacks: fertigation requires previous solid/liquid separation, acidification requires ad hoc equipment, and disc injection requires high mechanical traction. Full article

Originating from the practical demands of digital irrigation district construction, this study aims to provide support for precise irrigation management. This study developed a reinforcement learning-based intelligent irrigation decision-making model for districts employing traditional surface flood irrigation methods. Grounded in the theoretical framework of water cycle processes within the Soil–Crop–Atmosphere Continuum (SPAC) system and incorporating district-specific irrigation management experience, the model achieves intelligent and precise irrigation decision-making through agent–environment interactive learning. Simulation results show that in the selected typical area of the irrigation district, during the 10-year validation period from 2014 to 2023, the model triggered a total of 22 irrigation events with an average annual irrigation volume of 251 mm. Among these, the model triggered irrigation 18 times during the winter wheat growing season and 4 times during the corn growing season. The intelligent irrigation decision-making model effectively captures the coupling relationship between crop water requirements during critical periods and the temporal distribution of precipitation, and achieves preset objectives through adaptive decisions such as peak-shifting preemptive irrigation in spring, limited irrigation under low-temperature conditions, no irrigation during non-irrigation periods, delayed irrigation during the rainy season, and timely irrigation during crop planting periods. These outcomes validate the model’s scientific rigor and operational adaptability, providing both a scientific water management tool for irrigation districts and a new technical pathway for the intelligent development of irrigation decision-making systems. Full article

Agricultural land management is a major determinant of terrestrial carbon (C) fluxes and has substantial implications for greenhouse gas (GHG) mitigation strategies. This study evaluated the net ecosystem carbon balance (NECB) of an agricultural field in an organic integrated crop–livestock system (ICLS) with a ley-arable rotation in northern Germany over two years (2021–2023). Carbon dioxide (CO₂) fluxes were measured using the eddy covariance (EC) method to derive net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (RECO). This approach facilitated an assessment of the temporal dynamics of CO₂ exchange, alongside detailed monitoring of field-based C imports, exports, and management activities, of a crop sequence including grass-clover (GC) ley, spring wheat (SW), and a cover crop (CC). The GC ley acted as a consistent C sink (NECB: −1386 kg C ha⁻¹), driven by prolonged photosynthetic activity and moderate biomass removal. In contrast, the SW, despite high GPP, became a net source of C (NECB: 120 kg C ha⁻¹) due to substantial export via harvest. The CC contributed to C uptake during the winter period. However, cumulatively, it acted as a net CO₂ source, likely due to drought conditions following soil cultivation and CC sowing. Soil cultivation events contributed to short-term CO₂ pulses, with their magnitude modulated by soil water content (SWC) and soil temperature (TS). Overall, the site functioned as a net C sink, with an average NECB of −702 kg C ha⁻¹ yr⁻¹. This underscores the climate mitigation potential of management practices such as GC ley systems under moderate grazing, spring soil cultivation, and the application of organic fertilizers. To optimize CC benefits, their use should be combined with reduced soil disturbance during sowing or establishment as an understory. Additionally, C exports via harvests could be offset by retaining greater amounts of harvest residues onsite. Full article

Egypt, the world’s second-largest wheat importer, has been working hard to narrow the gap between its domestic wheat production and consumption. However, these efforts have been hampered by water scarcity and the negative impact of climate change on wheat production. This study seeks to analyze the influence of climatic and technical factors on wheat production in Egypt over the long and short term. Using Egypt-specific data from 1961 to 2022 and employing the Autoregressive Distributed Lag (ARDL) model and Granger-causality, the study examines the impact of factors such as harvested area, fertilizers, technology, CO₂ emissions, seasonal temperature and precipitation patterns (winter and spring) on wheat production in Egypt. The empirical results indicate that the harvested area, level of technology, and average winter temperature significantly and positively impact wheat production. Precisely, a 1% increase in these factors leads to a 1.08%, 1.49%, and 6.89% increase in wheat production, respectively. Conversely, a 1% rise in CO₂ emissions, average spring temperature, and precipitation reduced wheat production by 1.76%, 0.52%, and 0.054%, respectively. The Granger causality results indicate a bidirectional causal relationship between wheat production and harvested area. Furthermore, the technology level exhibits a significant causal influence on wheat production, cultivated area, and CO₂ emissions, highlighting its pivotal role in both the wheat production process and its environmental impact. In conclusion, this study is crucial for Egypt’s future food security. By identifying the key climatic and non-climatic factors that impact wheat production, policymakers can gain valuable insights to address climate change and resource limitations. Improving domestic production through technological advancements, effective resource utilization, and climate-resilient practices will ensure a sustainable food supply for Egypt’s expanding population in the face of global uncertainties. Full article

Kazakhstan cultivates over 12 million hectares of wheat, primarily spring wheat in the northern region. Spring wheat yields are low, ranging from 1.2 to 1.7 t/ha depending on weather conditions. Northern Kazakhstan is served by five spring wheat breeding programs: A.I. Barayev Research and Production Centre for Grain Farming and Agricultural Experimental Stations located in the Aktobe, Karagandy, Kostanay, and North Kazakhstan regions. In 2022, a germplasm set was assembled, including cultivars and breeding lines from the five breeding programs, totaling 84 genotypes. This set was evaluated in field trials during 2022 and 2023 at the breeding programs that contributed to the germplasm (except Aktobe). The material was also screened for molecular markers associated with genes for agronomic traits. The study objective was to compare the diversity and performance of germplasm originating from different breeding programs and identify potential underlying drivers. Breeding sites grouped based on variations in air temperature, precipitation, and grain yield demonstrated both similarities and differences among sites. However, these similarities were not reflected in the agronomic performance of materials originating from different locations. The expectation that germplasm would perform best for grain yield at its “home” location was not always confirmed. Grouping of germplasm based on genetic diversity of 20 molecular markers was not related to similarities in environmental conditions at the places of origin. The performance and diversity of germplasm from each of the five breeding programs is apparently driven by factors beyond environment, including breeding strategy and methodology, parental pool, and, in the absence of modern tools, breeders’ intuition and selection robustness. Kazakh spring wheat breeding programs require improvement to remain competitive in the face of increasing pressure from introduced foreign cultivars. Full article

Cephus fumipennis, a significant pest of highland spring wheat, damages crops through larval boring and feeding within wheat stalks. This activity disrupts nutrient and water transport, causing severe yield reductions. To find microbial biocontrol agents targeting this pest, primary entomopathogenic microorganisms were isolated and identified from naturally infected, deceased C. fumipennis larvae. Morphological examination and ITS-based phylogenetic analysis tentatively identified the isolate as the entomopathogenic fungus Clonostachys sp. (strain CF01). Third-instar larvae of C. fumipennis were inoculated with conidial suspensions of the CF01 strain at concentrations of 1 × 10⁵, 1 × 10⁶, 1 × 10⁷, and 1 × 10⁸ spores/mL. Spore suspensions of different concentrations demonstrated pathogenicity against third-instar larvae of C. fumipennis. The optimal growth conditions for strain CF01 were identified as follows: PPDA medium, 25 °C, fructose as the carbon source, and yeast extract as the nitrogen source. Photoperiod exhibited no significant effect on either mycelial growth or sporulation. These findings indicate that the CF01 strain possesses considerable potential for the biocontrol of C. fumipennis. Full article

Agrivoltaics represents a crucial technology and an innovative solution to promote sustainability. After a cropping season in an agrivoltaic system in Northern Italy, this study investigated the floristic composition and biomass of weed communities across three crops, evaluating their variation under shaded and full light conditions. In addition, the research assessed the role of uncultivated grassland areas in agrivoltaic-shaded conditions by quantifying their biomass and evaluating their potential feed value. Weed floristic diversity and biomass were surveyed at three different times. Soil and canopy parameters were analyzed in relation to photosynthetically active radiation (PAR). Grassland biomass was assessed after four different cuts and its suitability as a feed source was evaluated by the pastoral value and near infrared (NIR) spectroscopic analysis. Results showed that tomato had the lowest weed presence, and Setaria italica and Sorghum halepense were predominant in rice, while in durum wheat, higher nutrient availability favored Echinochloa crus-galli and Cirsium arvense. In weed community composition and biomass, no significant differences were observed for the effect of different light conditions (sun/shadow), and this may be attributed to their high environmental plasticity. PAR was strongly correlated with both soil and canopy temperatures. The analysis of floristic composition, biomass yield, pastoral value and nutritional quality of grassland vegetation indicated that spring cuts can be effectively used as forage, including for grazing. These findings suggest that integrating livestock activities could offer a win–win strategy for managing uncultivated areas within agrivoltaic systems, thereby enhancing their sustainability under organic farming practices. Full article

The manuscript explores the valorization of forest logging residues, collected during forest management operations between summer 2023 and spring 2025 in mixed deciduous and coniferous forests, as a raw material for producing valuable bioactive products. These products offer a sustainable alternative to synthetic pesticides and fertilizers. Seven batches of biomass, comprising understory trees and branches from deciduous (mainly aspen, birch, and grey alder) and coniferous (mainly Scots pine) species, were collected during different seasons, crushed, and extracted using an ethanol–water solution. The yield of hydrophilic extracts containing proanthocyanidins (PACs) ranged from 18 to 25% per dry biomass. The highest PACs concentration (42% of extract dry mass) was found in small branches with a high bark content. The extracts and PACs at concentrations of 6.25–12.50 mg mL⁻¹ showed fungicidal activity against several pathogenic fungi, including Botrytis cinerea Pers., Mycosphaerella sp. Johanson, Heterobasidion annosum (Fr.) Bref., and Heterobasidion parviporum Niemelä & Korhonen. Residual biomass after extraction, enriched with sea buckthorn berry pomace and a siliceous complex, was characterized and evaluated for its impact on the growth of Scots pine seedlings and selected agricultural crops. Results from forest and agricultural field trials in 2023–2025 confirmed a positive effect of the fertilizer on crop yield and quality at a low application rate (40 kg ha⁻¹ per crop). Fertilizer increased the yield of radish, dill, potatoes, and wheat by up to 44% (highest for potatoes and dill) compared to the reference, confirming its agronomic value. Full article

Wheat is a crucial crop for human nutrition, and the demand for high-quality indicators within the “from farm to fork” concept is increasing. Based on this premise, this study examined how, at the farm level, the fertilization system can influence key quality indicators relevant to wheat production and final products. This research was conducted under specific conditions of the Western Plain of Romania at the Agricultural Research and Development Station (ARDS), Lovrin, during 2015–2017. Fertilization involved the autumn application of phosphorus (concentrated superphosphate; 0, 40, 80, 120, 160 kg ha⁻¹ active substance, a.s.) and potassium (potassium chloride; 0, 40, 80, 120 kg ha⁻¹ a.s.). Nitrogen (ammonium nitrate; 0, 30, 60, 90, 120 kg ha⁻¹ active substance) was applied in spring in two stages. The combination of these three fertilizers resulted in 18 fertilized variants (T2 to T19), tested alongside an unfertilized control (T1). The experimental variants were arranged in four randomized replications. Grain quality was assessed based on protein content (PRO, %), gluten (GLT, g 100 g⁻¹), gliadins (Gliad, %), glutenins (Glut, g 100 g⁻¹), high-molecular-weight glutenins (HMW, g 100 g⁻¹), low-molecular-weight glutenins (LMW, g 100 g⁻¹), and the gliadin/glutenin ratio (Gliad/Glut). Compared to the average values for each indicator across the experiment, certain variants produced values above the mean, with statistical significance. Variant T16 stood out by producing values above the mean for all indicators, with statistical confidence. Multivariate analysis showed that five indicators with very strong (PRO, GLT) and strong (HMW, Glut, LMW) influence grouped in PC1, while two indicators (Gliad, Gliad/Glut) with very strong and strong influence grouped in PC2. The analysis revealed varying levels of correlation between the applied fertilizers, with nitrogen (N) showing very strong and strong correlations with most indicators, while phosphorus and potassium showed moderate-to-weak correlations. Regression analysis generated mathematical models that statistically described how each indicator varied in relation to the fertilizers applied. Full article

Climate change is expected to have significant effects on crop yield in the Canadian Prairies. The objective of this study was to investigate these possible effects on spring wheat, barley and canola production using the Decision Support System for Agrotechnology Transfer (DSSAT) modelling platform. We applied 21 climate change scenarios from high-resolution (0.22°) regional simulations to three modules, DSSAT-CERES-Wheat, DSSAT-CERES-Barley and CSM-CROPGRO-Canola, using a historical baseline period (1985–2014) and three future periods: near (2015–2040), middle (2041–2070), and far (2071–2100). These simulations are part of CMIP6 (Coupled Model Intercomparison Project Phase 6) and have been processed using statistical downscaling and bias correction by the NASA Earth Exchange 26 Global Daily Downscaled Projections project, referred to as NEX-GDDP-CMIP6. The calibration and validation results surpassed the thresholds for a high level of accuracy. Simulated yield changes indicate that climate change has a positive effect on spring wheat and barley yields with median model increases of 7% and 11.6% in the near future, and 5.5% and 9.2% in the middle future, respectively. However, in the far future, barley production shows a modest increase of 4.4%, while spring wheat yields decline significantly by 17%. Conversely, simulated canola yields demonstrate a substantial decrease over time, with reductions of 25.9%, 46.3%, and 62.8% from the near to the far future, respectively. Agroclimatic indices, such as Number of Frost-Free Days (NFFD), Heating Degree-Days (HDD), Length of Growing Season (GSL), Crop Heat Units (CHU), and Effective Growing Degree Days (EGDD), exhibit significant correlations with spring wheat. Conversely, precipitation indices, such as very wet days and annual 5- and 10-day maximum precipitation, have a stronger correlation with canola yield changes when compared with temperature indices. The results provide key guidance for policymakers to design adaptation strategies and sustain regional food security and economic resilience, particularly for canola production, which is at significant risk under projected climate change scenarios across the Canadian Prairies. Full article

Satellite monitoring of agricultural crops plays a crucial role in ensuring food security and in the sustainable management of agricultural resources, particularly in regions dominated by rainfed farming, such as the Turkestan region of Kazakhstan. Many satellite monitoring tasks rely on remote identification of different types of cultivated crops. In developing the proposed method, we accounted for the temporal characteristics of crop growth and development in various climatic zones of rainfed agriculture, analyzed the dynamics of the Normalized Difference Vegetation Index (NDVI) together with ground-based data, and identified effective time periods and patterns for successful crop recognition. This study aims to develop and comparatively assess two methods for the automatic identification of cultivated crops in rainfed zones using Sentinel-2 satellite data for the years 2018 and 2022. The first method is based on detailed classification of pre-digitized field boundaries, providing high accuracy in satellite-based mapping. The second method represents a fully automated approach applied to large rainfed areas, emphasizing operational efficiency and scalability. The results obtained from both methods were validated against official national statistics, ground-based field surveys, and farm-level data. The findings indicate that the field-boundary-based method delivers significantly higher accuracy (average accuracy of 91.1%). While the automated rainfed-zone approach demonstrates lower accuracy (78%), it still produces acceptable results for large-scale monitoring, confirming its suitability for rapid assessment of sown areas. This research highlights the trade-off between the accuracy achieved through detailed field boundary digitization and the efficiency provided by an automated, scalable approach, offering valuable tools for agricultural production management. Full article

Wheat is a staple crop widely sown in Northwest China, and understanding and modelling evapotranspiration (ET) during the wheat-growing stage is important for irrigation scheduling and the efficient use of agricultural water resources. In this study, a four-year observation was conducted on a spring wheat field with border irrigation (BI) treatment and drip irrigation (DI) treatment, based on two Bowen ratio energy balance (BREB) systems. The results showed that the average ET across the whole growing stage scale was 512.0 mm for the BI treatment and 446.9 mm for the DI treatment, and the DI treatment reduced ET by 65.1 mm across the growing stage scale. The driving factors of the changes in ET in the two treatments were investigated using partial correlation analysis after understanding the changing pattern of ET. Net radiation (Rn), soil water content (SWC), and leaf area index (LAI) were the main meteorological, soil, and crop factors leading to the changes in ET in the two treatments. In terms of ET simulation, the SWAP model and different types of machine learning algorithms were used in this study to numerically simulate ET at a daily scale. The total ET values simulated by the SWAP model at the interannual scale were 11.0–14.2% lower than the observed values of ET, and the simulation accuracy varied at different growing stages. In terms of the machine learning simulation of ET, this study is the first to apply five machine learning algorithms to simulate a typical irrigated wheat field in the arid region of Northwest China. It was found that the Stacking algorithm as well as the SWAP model had the optimal simulation among all machine learning algorithms. These findings can provide a scientific basis for irrigation management and the efficient use of agricultural water resources in spring wheat fields in arid regions. Full article