Agriculture, as the largest consumer of freshwater, plays a crucial role in managing limited water resources. However, uncertainties in water supply, particularly in water-scarce regions, hinder further agricultural development. Balancing agricultural development with environmental sustainability is especially demanding in the Karkheh River Basin (KRB), western Iran. Traditional irrigation networks in the upper KRB are inefficient, exacerbated by unregulated water use and over-extraction. This study explores the Water Supply-Demand Balance (WSDB) dynamics under current cropping patterns in the semi-arid Honam sub-basin of the upper KRB. Time-series Landsat imagery and Machine Learning (ML) algorithms classified crop types and estimated major crop cultivation areas during 2019–2021. Regional Crop Water Requirement (ETcreg) and Regional Net Irrigation Requirement (INETreg) estimated the Regional Gross Irrigation Water Demand (GIWDreg), while inflow-outflow analyses determined Surface Irrigation Water Supply (SIWS). The study achieved high crop classification accuracy, facilitating precise water demand estimation across large-scale traditional irrigation networks. Results indicated the highest GIWDreg in 2020 (11.3 × 106 m3/year), driven by increased forage cultivation with crop water requirements (ETc) exceeding 1000 mm. That year also recorded the maximum SIWS (14.8 × 106 m3/year). The Water Scarcity Index (WSI) classified shortages as severe to extreme, indicating WSDB instability due to drought and cropping pattern shifts. This study highlights the necessity for government initiatives promoting water-efficient cropping to align agriculture with water availability. The proposed approach offers practical insights for policymakers seeking to optimize food security while ensuring sustainable water management in traditional irrigation networks, where cropping patterns are difficult to assess.

Grazing is a crucial component of dairy farms across many regions of the world. This review explores challenges related to grazing infrastructure and opportunities for future improvement. Farmers who aim to increase pasture utilisation face heightened inter-animal competition necessitated by pasture restriction to achieve target post-grazing sward heights. Increasing the frequency of fresh pasture allocation beyond once per day has been observed to reduce milk production in primiparous animals, due to intensified competition for limited feed resources. Implementing grazing paddocks tailored for 24- to 36-hour allocations helps to mitigate inter-animal competition while concurrently preventing the grazing of fresh regrowth. Crucial to this approach is establishing farm roadway infrastructure that allows access to all sections of the grazing platform. However, the development of these roadway networks has often occurred without a comprehensive assessment of their impact on the efficiency of the dairy herd’s movement between grazing paddocks and the milking parlour. The efficiency of the dairy herd’s movement is most significantly influenced by the location of the milking parlour within the grazing platform. Extreme walking distances or challenging terrain on farm roadways may have an impact on milk production per cow. Factors such as farm roadway surface quality and width significantly influence cow throughput on farm roadways. Recent studies have highlighted inadequate roadway widths on many farms relative to their herd size, while surface condition may also be limiting cow throughput on these farms. Enhancing roadway width and surface condition of farm roadways may improve labour efficiency on commercial farms.

The concept of interaction classes (iClasses) for multi-environment trial data was introduced to address the problem of summarising variety performance across environments in the presence of variety by environment interaction (VEI). The approach involves the fitting of a factor analytic linear mixed model (FALMM), with the resultant estimates of factor loadings being used to form groups of environments (iClasses) that discriminate varieties with different patterns of VEI. It is then meaningful to summarise variety performance across environments within iClasses. The iClass methodology was developed with respect to a FALMM in which the genetic effects for different varieties were assumed independent. This was done for pedagogical reasons but it was pointed out that the accuracy of variety selection is greatly enhanced by considering the genetic relatedness of varieties, either via ancestral or genomic information. The focus of the current paper is therefore to extend the iClass approach for FALMMs which incorporate such information. In addition, a measure of stability of variety performance across iClasses is defined. The utility of the approach for variety selection is illustrated using a multi-environment trial dataset from the lentil breeding programme operated by Agriculture Victoria.

The study analysed corn harvested areas from 1960 to 2030, using data from the USDA open-access official dataset and 1000 autoregressive integrated moving average models. The global corn harvested area experienced a steady growth rate of 1.0 % in the 1960s, followed by a slight setback in the 1980s and a recovery in the 1990s. The early 2000s saw significant expansion, while the 2010s saw a slightly slower pace of 1.4 %. From 2020 to 2024, the global growth rate declined to 0.0 %, suggesting a potential shift towards a more stable or slower growth phase. The most likely scenario for the global corn area is an optimistic outlook with a 37 % likelihood of steady growth. China’s most likely scenario is a gradual expansion with a rate of 2.0 %. The United States’ most likely scenario suggests a neutral trend. Brazil has remarkable growth trajectories in its areas, reflecting land availability. Ukraine’s area by 2030 is projected to experience varying outcomes due to uncertainties in the agricultural landscape.

Soil health is essential for a resilient ecosystem. The European Union proposed a Soil Monitoring Law for a legal framework of soil health. This study proposes a way to assess the mineral soil health. A database of mineral soils containing <20% organic matter and consisting of 10 soil classes and 22 soil types was used. There were four altitudinal groups (HM [high mountains], LM [low mountains and high hills], LH [low hills], PL [low plains]), covering the vegetation/climate floors, two land uses (forestland and grassland combined and cropland) and three soil textures (coarse [CO], loamy [LO] and clayey [CL]). Both soil organic carbon (SOC)/clay ratios and observed per mean SOC (O/M SOC) ratios were calculated for 19 regions. For SOC/clay, the 1/13, 1/10 and 1/8 thresholds were used, whereas O/M SOC categories were grouped as ‘low’, ‘intermediate’, ‘high’ and ‘very high’ health. SOC/clay and O/M SOC ratios combined were used to characterize soil health. SOC sequestration depends on many factors that are specific for each pedo-climatic region and texture and so is the soil characterization as healthy or not healthy. The recommended simultaneous application of these two indicators revealed specific SOC content values as reference levels for good soil health, which decrease from the wetter climates towards the drier ones. SOC content considerably differed among pedo-climatic regions, and soil health should be compared within the same regions that have specific SOC sequestration conditions. Correlations between support points SOC values and the aridity index allow separation between ‘healthy’ and ‘non-healthy’ soil conditions for any climate, vegetation floor and land use.

The facilities where the livestock carry their production cycle must have as their main characteristic the control of the influence of variation of environmental factors on the animals, which can be controlled through the use of different ventilation systems. In this way, the objective of this research was to evaluate the influence of different conditioning systems in the swine farrowing house. Three treatments were tested in a farrowing house located in Parana, Brazil: refrigerated ventilation (T1), forced ventilation (T2) and natural ventilation (T3), on 12 females with 11 piglets each. The climatic parameters evaluated were temperature, velocity and relative humidity of the air. For the females were analysed the parameters: surface temperature (laser thermometer and thermal image), average weight and feed conversion at the end of lactation; and for the piglets: surface temperature (thermal image), average weight and weekly feed conversion. The data were interpreted statistically by analysis of variance and Tukey’s test with 5% probability of error, using the program SAS (2008). The use of the refrigerated ventilation system decreased the temperature of the air and the superficial temperature of the females, without affecting the comfort of the piglets. There was no difference in the productive indexes of the animals, thus other aspects of the production chain could be explored to reflect a higher financial gain.

The objective was to evaluate the influence of supplementation of L-arginine at 10 g/kg on the performance of lactating sows and their litters by evaluating the milk amino acid composition, serum hormone levels, mammary tissue redox status indicators, mammary tissue histomorphology, and the expression of genes related to mammary gland metabolism. The study was conducted in the municipality of Oliveira, Minas Gerais, Brazil, in 2019. A total of 24 sows were selected. A completely randomized design was used in a 2 × 3 factorial arrangement, with two diets (control and 10 g L-arginine/kg) and three mammary gland positions (thoracic, abdominal and inguinal), totaling six treatment groups. Each dietary treatment consisted of 12 replicates, with each replicate equivalent to one experimental unit, which was considered a sow and its litter. Females that were fed a diet supplemented with L-arginine had lower feed intake and greater weight loss. Piglets from sows that received L-arginine supplementation presented reduced weaning weight variation. L-arginine supplementation did not affect the amino acid composition of the milk, the plasma hormone levels of the sows, the oxidative stress parameters of redox, or breast tissue morphology. However, it may modulate the expression of genes important for mammary gland metabolism, such as the COX1 and SLC27A4. Piglets that suckled teats in the thoracic and abdominal positions had higher mean weights at 14 and 23 days, as well as greater daily weight gains. L-arginine supplementation to sows improves litter quality by increasing weaning weight uniformity; piglets that occupy the thoracic and abdominal positions perform better.

The aim of this study was to estimate genetic parameters for dairy traits (daily milk yield (DMY), daily milk fat yield (DMFY), and daily milk protein yield (DMPY)) and somatic cell score (SCS) of Iranian Murciano-Granadina goats. The analysis involved 99,839 records from 4,307 does, spanning from 2015 to 2021. A single-trait animal model was used to obtain estimates of genetic and residual (co)variances and heritabilities. In the model, both fixed effects (birth year, parity, litter size, age dam (day)) and random effects (additive animal genetic effects) were considered. R software was used to evaluate environmental effects, assess the model, examine genetic and phenotypic trends, and determine genetic-environmental correlations. Additionally, variance components and heritability were estimated using the Gibbs1f90 software. The heritability of dairy traits and SCS trait, namely DMY, DMFY, DMPY, and SCS, was estimated to be 0.244, 0.133, 0.188, and 0.023, respectively. The genetic correlations between DMY and DMFY, DMY and DMPY, DMY and somatic cell score (SCS), DMFY and DMPY, DMFY and SCS, and DMPY and SCS were 0.07, 0.16, 0.42, –0.08, –0.005, and 0.04, respectively. The study found moderate heritability for milk yield traits and low heritability for somatic cell score. Genetic correlations varied, with the strongest correlation between daily milk yield and somatic cell score, indicating useful genetic linkages for selective breeding.