Search Results (179)

Pea (Pisum sativum L.) is a multifunctional legume of growing importance in sustainable cropping systems. This study presents an integrative assessment of a forage pea variety across multiple agronomic functions under temperate continental conditions. Results from three environmentally comparable field trials were synthesized to evaluate (i) grain yield and protein traits, (ii) biomass production and nutrient accumulation in cover cropping systems, and (iii) effects on soil nitrate dynamics and maize (Zea mays L.) yield. Compared with vegetable- and dry-seed-type genotypes, the forage-type cultivar exhibited greater plant height and lodging tendency, moderate grain yield, and elevated protein content (28.8%), characterized by a legumin-dominated protein profile. As a winter cover crop grown in mixture with oat (Avena sativa L.), pea produced lower total biomass than rye (Secale cereale L.) but showed substantially higher nitrogen concentrations (2.93–3.01%), indicating enhanced nitrogen input potential. In crop rotation, pea-based treatments significantly affected soil nitrate distribution and maize productivity. Complementary resource use in pea-based systems enhanced biomass production, supporting forage and green manure functions while contributing to soil fertility and system stability. Its morphological and physiological adaptability enables integration into diverse production models, from intensive to regenerative systems. Overall, pea should be regarded not merely as a single crop, but as a strategic component of diversified farming systems aimed at increasing protein yield, optimizing inputs, improving soil quality, and strengthening the long-term sustainability of agroecosystems. Full article

Remote sensing has become a cornerstone of modern agricultural monitoring, addressing the dual challenges of increasing production while ensuring environmental sustainability. Based on a conceptual framework developed over the past decade, key application areas include yield estimation, phenology, stress assessment (e.g., drought), crop mapping, and land-use change detection. In Central Europe, regionally specific conditions such as fragmented land ownership, small and irregular plots, and high climate variability shape these applications. Annual field crops, such as cereals, oilseeds, maize, and forage crops dominate production and represent the primary focus of monitoring efforts. Optical data from Sentinel-2 are effective for mapping crop types and analyzing phenology, especially when dense time series are available. However, persistent cloud cover during critical growth phases limits the effectiveness of optical approaches, prompting the integration of radar data from Sentinel-1. Multi-sensor strategies increase the robustness of classification and temporal continuity, supporting monitoring under adverse conditions. Reliable reference data from systems such as the Land Parcel Identification System enables parcel-level validation and facilitates object-oriented analyses in line with management needs. Future developments will increasingly rely on advanced time-series analysis, machine learning, and the integration of agrometeorological and crop model data. As climate change intensifies drought frequency and yield variability, remote sensing will play a pivotal role in enabling near-real-time monitoring and decision support within the evolving landscape of digital agriculture ecosystems. The aim of this review article is to provide an overview of crop monitoring in the Central European region over approximately the past fifteen years, emphasizing trends in subsequent technological and procedural developments. Full article

The quality of beef fat depends on both intramuscular fat (IMF) content and fatty acid (FA) composition, which can be modulated by finishing diets. This study evaluated the effects of silage type and feeding intensity on IMF deposition, FA profile, desaturase indices, and lipid quality indices in finishing Holstein–Friesian bulls. Thirty-two bulls were assigned to a 2 × 2 factorial design (n = 8/group) and fed total mixed rations for 120 days based on grass silage or maize silage, under intensive (≈50:50 forage:concentrate, DM basis) or semi-intensive feeding (≈70:30). FA composition of longissimus lumborum lipids was determined by GC-FID, and lipid quality indices were calculated, including the atherogenic index (AI), thrombogenic index (TI), and the hypocholesterolemic/hypercholesterolemic ratio (h/H). Feeding intensity increased IMF content (p = 0.001) and the absolute amounts of major FA classes (g/100 g meat). Silage type primarily affected FA composition by increasing n-3 PUFA and lowering the n-6/n-3 ratio in grass silage diets (p = 0.042). Several FAs showed silage type × feeding intensity interactions (p < 0.05), indicating that the response to dietary energy supply depended on the forage base. Overall, feeding intensity mainly regulated lipid deposition, whereas silage type modulated the nutritional profile of intramuscular fat. Full article

Climate change has put the agricultural industry under enormous pressure, as rising temperatures and changing rainfall patterns are affecting crop yields and productivity. The temporal variability of the irrigation water requirement (IWR) as a function of crop evapotranspiration (ETc) and effective rainfall (Peff) was analyzed for forage corn cultivation from a climate-change perspective in the “Comarca Lagunera” region, located in the north of Mexico. The time periods 1975–2016 and 2061–2080 were analyzed, the latter using the forcings of the climate-change scenario SSP5-8.5, from the meteorological data. The Peff, ETc, and IWR for the maize crop were modeled with CROPWAT software, and the Rodionov test was applied to detect points of change in the three variables mentioned above. The historical values of IWR, ETc, and Peff values for spring were estimated at 511, 571, and 57, while for summer, they were 336, 450, and 122, respectively. The climate-change scenario toward the distant horizon projects increases in IWR of 11.9% and 3.5% and in ETc of 7.7% and 0.6%, respectively, for both spring and summer agricultural cycles, as well as decreases in Peff of −30% and −12%, respectively. These results emphasize the combined impact of rising temperatures and reduced rainfall on crop water needs, a crucial factor for crop production in regions that depend on agricultural irrigation. This study provides a foundation for planning irrigation water management in anticipation of an imminent increase in demand due to erratic weather patterns in arid zones. Full article

Optimizing nitrogen fertilization and planting date is essential for improving forage maize productivity under semi-arid conditions. This study evaluated the effects of nitrogen application rates and planting dates on growth, forage yield, and quality of maize (Zea mays L.) in Upper Egypt. A two-year field experiment (2024–2025) was conducted at the Experimental Farm of Assiut University using a strip-plot design arranged in a randomized complete block design with three replications. Four planting dates (15 April, 15 May, 15 June, and 15 July) were assigned horizontally, while three nitrogen rates (167, 238, and 309 kg N ha⁻¹) were applied vertically. Growth traits, fresh and dry forage yield, dry matter percentage, crude protein content, and protein yield were recorded at 60 days after sowing. Results showed that planting date, nitrogen rate, and their interaction significantly affected most measured traits in both seasons. Sowing in mid-May consistently produced the highest plant height, chlorophyll content, fresh and dry forage yield, and protein yield. Increasing nitrogen application enhanced biomass production and forage quality, with the highest values generally recorded at 309 kg N ha⁻¹. The strongest yield response to nitrogen occurred when maize was sown at the optimal planting date, indicating that nitrogen utilization was closely linked to favorable environmental conditions. Phenotypic correlation and multivariate analyses revealed strong associations among vegetative growth traits and forage yield, with a single dominant factor explaining more than 91% of the variation in yield-related traits across seasons. Overall, the results demonstrate that synchronizing planting date with appropriate nitrogen fertilization is critical for maximizing maize forage yield and quality under semi-arid conditions. Mid-May sowing combined with adequate nitrogen supply represents an effective management strategy for forage maize production in Upper Egypt, while further research is needed to optimize nitrogen-use efficiency and long-term sustainability. Full article

Extreme weather impacts the ecological niches of fungi, altering mycotoxin risks in wildlife. We analyzed mycotoxin carry-over into European fallow deer (Dama dama) milk across seasons and assessed how drought influences the shift from Fusarium to Aspergillus mycotoxins and affects physiological resilience. Samples were collected during 2021/2022 and a drought-stricken 2022/2023 from South Transdanubia and Northeastern Hungary. Aflatoxin B1/M1 (AFB1/AFM1), Fumonisin B1 (FB1), Deoxynivalenol (DON), Zearalenone (ZEN), and Body Condition Scores (BCS) were measured to evaluate the impact of exposure on health status. The severe drought significantly altered the mycotoxin profile: ZEN levels declined significantly (from a median of 0.28 to 0.00 ng/mL), consistent with the moisture requirements of Fusarium graminearum, whereas DON concentrations increased. Concurrently, AFM1 persisted, exhibiting increased variance and extreme outliers in the maize-dominated South Transdanubian region. Distinct pharmacokinetic patterns were observed, and positive correlations were observed between milk and feces for lipophilic toxins, validating milk as a possible biomarker. Hydrophilic DON showed no correlation despite its accumulation. Emergence of “Poor” BCS group carrying loads supports “condition-dependent foraging” hypothesis, as stressed individuals are forced to consume contaminated resources, exacerbating oxidative stress and metabolic deficits. Full article

Whole-plant maize (corn) (WPC) is a critical forage in ruminant diets, and rapid, reliable measurement of its nutritional composition is essential for precision feeding. We hypothesized that an on-site near-infrared spectroscopy (OS-NIRS—specifically, HarvestLab™ 3000) sensor would provide within-laboratory repeatability comparable to commercial analytical laboratories (ALs) and inter-laboratory reproducibility similar to conventional laboratory analyses. To test this, WPC samples were collected across three experiments and two countries (USA and Germany) and analyzed by both OS-NIRS and ALs, with precision metrics calculated according to ISO 5725. Results showed that OS-NIRS achieved intra-laboratory repeatability equal to or greater than ALs, particularly for protein and starch. The repeatability performance of the OS-NIRS sensors was similar to that of ALs for moisture and NDF. Inter-laboratory reproducibility varied widely across constituents and experiments. Including OS-NIRS data with AL measurements produced inconsistent effects—sometimes narrowing confidence intervals but more often widening them—while OS-NIRS data alone demonstrated repeatability on par with ALs but mixed reproducibility outcomes. Inclusion of OS-NIRS data did not introduce systematic bias and, in some cases, improved consistency. These findings indicate that OS-NIRS can complement laboratory analyses by providing timely, farm-level measurements that enhance decision-making in feed management. Full article

This paper investigates whether participation in Zambia’s social cash transfer programme (SCTP) improves household dietary diversity among ultra-poor rural households. While cash transfers are widely implemented across sub-Saharan Africa as social protection measures, empirical evidence regarding their impact on nutritional status remains mixed. This study focuses on dietary diversity, a proxy for nutrition quality, and uses data from the 2015 Rural Agricultural Livelihood Survey (RALS). The analysis employs propensity score matching to control for demographic differences between recipient and non-recipient households, followed by a regression analysis to examine the association between SCTP participation and dietary diversity scores. The findings reveal no statistically significant association between receiving social cash transfers and higher household dietary diversity. In contrast, positive predictors of dietary diversity included household remittances, own production of animal-source foods, and maize sales. Notably, households that relied on foraging exhibited significantly lower dietary diversity, suggesting foraging may be a coping strategy among food-insecure households. These results imply that while the SCTP may enhance household income stability, it does not necessarily translate into improved diet quality. This study contributes to the ongoing policy debate on the effectiveness of cash-based interventions in improving nutrition outcomes. It highlights the need to complement cash transfers with interventions that support food production and access, particularly in rural settings where market and infrastructure limitations persist. Full article

To reconcile the intensifying trade-off between chronic water scarcity and escalating forage demand in the Yellow River Basin, this study optimized integrated irrigation and fertilization regimes for silage maize. Leveraging the AquaCrop model, validated by 2023–2024 field experiments and a 35-year (1990–2024) meteorological dataset, we systematically quantified the impacts of multi-factorial water–fertilizer–heat stress under drip irrigation with mulch (DIM) and shallow-buried drip irrigation (SBDI). Model performance was robust, yielding high simulation accuracy for soil moisture (RMSE < 3.3%), canopy cover (RMSE < 3.95%), and aboveground biomass (RMSE < 4.5 t·ha⁻¹), with EF > 0.7 and R² ≥ 0.85. Results revealed distinct stress dynamics across hydrological scenarios: mild temperature stress predominated in wet years, whereas severe water and fertilizer stresses emerged as the primary constraints during dry years. To mitigate these stresses, a medium fertilizer rate (555 kg·ha⁻¹) was identified as the stable optimum, while dynamic irrigation requirements were determined as 90, 135, and 180 mm for wet, normal, and dry years, respectively. Comparative evaluation indicated that DIM achieved maximum productivity in wet years (aboveground biomass yield 70.4 t·ha⁻¹), whereas SBDI exhibited superior “stable yield–water saving” performance in normal and dry years. The established “hydrological year–irrigation method–threshold” framework provides a robust decision-making tool for precision management, offering critical scientific support for the sustainable, high-quality development of livestock farming in arid regions. Full article

The Lagunera Region, located in northern Mexico, is home to the country’s most important dairy basin, situated in a semi-arid environment. In this region, forage corn (Zea mays L.) is the main input in dairy cattle feed. In this context, optimizing water use and nitrogen nutrition is a priority to ensure the sustainability of this activity. The main objective of this study was to evaluate the productivity and water use efficiency of forage corn under different humidity, nitrogen, and substrate type levels. A randomized block design with sub-subdivided plots was used. The larger plot contained two usable moisture levels (80 and 50%); the subplots were assigned according to three nitrogen levels: 13.6 (N1), 6.8 (N2), and control 0.35 (N3) NO₃ mmol·L⁻¹; the sub-subplots were assigned based on two substrates: sand and a mixture (MI) of sand, perlite, and peat moss. The results showed significant triple interactions (p < 0.05) in the root volume traits, where nitrogen played a determining role, as well as double interactions (Nutrition*Substrate) for all vegetative and radicle production variables and water use efficiency. Principal components analysis explained 91.4% of the total observed variation, where basal diameter had the vector with the highest load value. Cluster analysis identified that the main discriminant factor was nutrition. It is concluded that usable moisture levels up to 50% with 6.8 mmol·L⁻¹ of NO₃ show acceptable levels of vegetative production and root volume in forage corn. These results suggest the possibility of reducing water and nitrogen fertilizer consumption without compromising yield, with significant economic and environmental benefits for agriculture in arid and semi-arid regions. Full article

Mycotoxins are global contaminants of feedstuffs and feeds that are linked to animal health and performance challenges and subsequently lead to economic burden. Negative effects of mycotoxin consumption may increase as a result of multiple mycotoxin co-occurrences. To assess mycotoxin challenge in Europe, a seven-year survey (2018 to 2024) of 1867 samples of grains (barley, maize, and wheat) and 818 forages (maize silage and grass silage) was conducted to assess the simultaneous presence of 54 mycotoxins using ultra-pressure liquid chromatography–tandem mass spectrometry. Results were categorized by feedstuff, harvest year, and climatic region to gain insight on mycotoxin occurrence, concentration and co-occurrence. Grains contained a mean 3.6 to 6.7 mycotoxin types per sample, while silages contained 3.1 to 6.0. Barley in the Nordic climate region had some of the highest Fusarium mycotoxin concentrations, while maize silage had consistently higher mycotoxin concentrations across all climate regions. The B trichothecenes and emerging mycotoxins had the highest rates of co-occurrence (52.4% to 74.2% of samples) in grains and maize silage. Co-occurrence data can serve as an initial framework for identifying or reasserting known environmental conditions that favor mycotoxin biosynthesis in distinct fungal taxa and for refining risk assessment of animals simultaneously exposed to multiple mycotoxins. Collectively, this survey shows that mycotoxin contamination and co-occurrence in grains and silages from Europe is expected, with differences occurring by feedstuff type and climatic region. Full article

Maize–alfalfa intercropping is practiced in Northeast China to improve land productivity and forage production. However, competition between the two crops can reduce system performance, which calls for an emphasis on optimal row ratio. Hence, the current study evaluated the effects of diverse maize–alfalfa row ratio configurations (1:1, 2:1, 2:2, 3:1, 3:2, and 3:3) on resource-use efficiency, physiological traits, and yield performance. It was noted that the mono-cropping system had higher physiological and agronomic values for both crops. With regard to the intercropping configuration, the 2:2 steadily outperformed all other intercropping row ratios. Whereas alfalfa grew tallest in 2:2, maize plant height peaked under the 3:1. Photosynthetic rate and chlorophyll content were highest under 2:2, for both crops. The yield results indicated that alfalfa achieved maximum forage and biomass, whereas maize performed best under a 3:1 configuration. Outstandingly, under the 2:2 ratio, the cumulative system yield exceeded alfalfa mono-cropping by 55% and maize mono-cropping by 56–57%. There was superior complementarity and land-use advantage under 2:2, as indicated by the highest resource-use indicators of LER (land equivalent ratio), LEC (land equivalent coefficient), SPI (system productivity index), and K (crowding index). Competitive Indices showed that competition was more balanced under 2:2, with maize dominating in systems with higher maize proportions. Overall, the 2:2 row ratio provided the best balance of reduced competition and enhanced complementarity, offering a more efficient and sustainable maize-alfalfa intercropping strategy. Full article

Insufficient precipitation and low temperatures can restrict grain yield but not necessarily vegetative growth in cold–arid regions. This indicates that forage production may be more suitable than grain cultivation in these environments while also meeting the increasing demand for livestock products. In this study, we compared the effects of cultivating forage maize (Zea mays L.) and forage oat (Avena sativa L.) with those of traditional grain crops, such as potato (Solanum tuberosum L.) and wheat (Triticum aestivum L.), in terms of aboveground biomass, crude protein yield, and water use efficiency (WUE). Across the four-year study, the results showed that aboveground biomass increased by 26–125% with oat (9.10 t ha⁻¹) and maize (13.7 t ha⁻¹) cultivation compared to potato (7.23 t ha⁻¹) or wheat (6.10 t ha⁻¹). Maize and potato exhibited greater biomass stability due to longer growing seasons and better synchronization with peak precipitation. In contrast, wheat and oat exhibited higher biomass variability, reflecting their susceptibility to early spring drought. Among the four crops analyzed, maize achieved the highest crude protein yield (1068 kg ha⁻¹) and WUE (31.9 kg biomass ha⁻¹ mm⁻¹), primarily due to its superior biomass production rather than its protein concentration or elevated soil water consumption. Therefore, cultivating forage crops with longer growth periods could effectively align water demand with seasonal precipitation, thereby improving biomass accumulation and WUE in hydrothermally limited regions. Full article

Intensifying urbanization in Central Europe is increasingly pushing flood retention areas onto private farmland, yet the agronomic and socio-economic trade-offs remain poorly quantified. We conducted a narrative review of published field data and post-event assessments from 2014–2023 along the transboundary Sava River. Information was collected from research articles, case studies, and environmental monitoring reports, and synthesized in relation to national and EU regulatory thresholds to evaluate how floods altered soil functions and agricultural viability. Water erosion during floods stripped up to 30 cm of topsoil in torrential reaches, while stagnant inundation deposited 5–50 cm of sediments enriched with potentially toxic elements, occasionally causing food crops to exceed EU contaminant limits due to uptake from the soil. Flood sediments also introduced persistent organic pollutants: 13 modern pesticides were detected post-flood in soils, with several exceeding sediment quality guidelines. Waterlogging reduced maize, pumpkin, and forage yields by half where soil remained submerged for more than three days, with farm income falling by approximately 50% in the most affected areas. These impacts contrast with limited public awareness of long-term soil degradation, raising questions about the appropriateness of placing additional dry retention reservoirs—an example of nature-based solutions—on agricultural land. We argue that equitable flood-risk governance in the Sava River Basin requires: (i) a trans-boundary soil quality monitoring network linking agronomic, hydrological, and contaminant datasets; (ii) compensation schemes for agricultural landowners that account for both immediate crop losses and delayed remediation costs; and (iii) integration of strict farmland protection clauses into spatial planning, favoring compact, greener cities over lateral river expansion. Such measures would balance societal flood-safety gains with the long-term productivity and food security functions of agricultural land. Full article

Maize (Zea mays L.) silage’s forage quality significantly impacts dairy animal performance and the profitability of the livestock industry. Recently, using uncrewed aerial vehicles (UAVs) equipped with advanced sensors has become a research frontier in maize high-throughput phenotyping (HTP). However, extensive existing studies only consider a single sensor modality and models developed for estimating forage quality are single-task ones that fail to utilize the relatedness between each quality trait. To fill the research gap, we propose MUSTA, a MUlti-Sensory feature fusion model that utilizes MUlti-Task learning and the Attention mechanism to simultaneously estimate dry matter yield and multiple nutritive values for silage maize breeding hybrids in the field environment. Specifically, we conducted UAV flights over maize breeding sites and extracted multi-temporal optical- and LiDAR-based features from the UAV-deployed hyperspectral, RGB, and LiDAR sensors. Then, we constructed an attention-based feature fusion module, which included an attention convolutional layer and an attention bidirectional long short-term memory layer, to combine the multi-temporal features and discern the patterns within them. Subsequently, we employed multi-head attention mechanism to obtain comprehensive crop information. We trained MUSTA end-to-end and evaluated it on multiple quantitative metrics. Our results showed that it is capable of practical quality estimation results, as evidenced by the agreement between the estimated quality traits and the ground truth data, with weighted Kendall’s tau coefficients (τ_w) of 0.79 for dry matter yield, 0.74 for MILK2006, 0.68 for crude protein (CP), 0.42 for starch, 0.39 for neutral detergent fiber (NDF), and 0.51 for acid detergent fiber (ADF). Additionally, we implemented a retrieval-augmented method that enabled comparable prediction performance, even without certain costly features available. The comparison experiments showed that the proposed approach is effective in estimating maize silage yield and nutritional values, providing a digitized alternative to traditional field-based phenotyping. Full article