Strategic management of crop residues is essential to enhance soil quality for sustainable agriculture. However, little is known about the specific amounts of crop residues needed to improve soil quality characteristics which are key to develop economic plans. In this study, we investigated the effects of applying crop residue at five rates, including 100% (R₁₀₀), 75% (R₇₅), 50% (R₅₀), 25% (R₂₅), and 0% (R₀), on wheat yield and soil properties. Field experiments were conducted for two cropping seasons in a wheat-corn rotation under conventional (CT) and no-till (NT) systems to observe the first results obtained during short-term periods (one-year application). During the study, the wheat and corn fields were irrigated. Application of plant residue resulted in increased soil organic carbon (SOC) and available nutrients and improved soil physical properties, i.e., aggregates mean weight diameter in wet (MWDw) and dry (MWD_d) conditions, water-stable aggregates (WSA), dry-stable aggregates, (DSA), soil water infiltration (SWI), soil available water (SAW), and yield of wheat and corn. The effects were stronger at higher residue application rates. In the CT system, compared to R₀, R₁₀₀ resulted in the highest increase equal to 38, 29, 23, 34, 35, 41, and 11% for SOC, MWDw, MWD_d, WSA, DSA, SAW, and wheat grain yield, respectively. This was equivalent to 28, 19.5, 19, 37, 44, 52, and 6% for the NT system, respectively. Generally, the NT system resulted in a stratification of the soil properties within 0–10 cm compared to 10–20 cm soil depth, but a uniform distribution for both depths under CT system. Overall, these results show that crop residue application can improve soil quality and yield in cereal production systems under semi-arid conditions during the first year of application. It will be key to monitor these changes in along-term field studies.

Recently, the demand for high-quality land use/land cover (LULC) information for near-real-time crop type mapping, in particular for multi-relief landscapes, has increased. While the LULC classes are inherently imbalanced, the statistics generally overestimate the majority classes and underestimate the minority ones. Therefore, the aim of this study was to assess the classes of the 10 m European Satellite Agency (ESA) WorldCover 2020 land use/land cover product with the support of the Google Earth Engine (GEE) in the Honam sub-basin, south-west Iran, using the LACOVAL (validation tool for regional-scale land cover and land cover change) online platform. The effect of imbalanced ground truth has also been explored. Four sampling schemes were employed on a total of 720 collected ground truth points over approximately 14,100 ha. The grassland and cropland totally canopied 94% of the study area, while barren land, shrubland, trees and built-up covered the rest. The results of the validation accuracy showed that the equalized sampling scheme was more realistically successful than the others in terms of roughly the same overall accuracy (91.6%), mean user’s accuracy (91.6%), mean producers’ accuracy (91.9%), mean partial portmanteau (91.9%) and kappa (0.9). The product was statistically improved to 93.5% ± 0.04 by the assembling approach and segmented with the help of supplementary datasets and visual interpretation. The findings confirmed that, in mapping LULC, data of classes should be balanced before accuracy assessment. It is concluded that the product is a reliable dataset for environmental modeling at the regional scale but needs some modifications for barren land and grassland classes in mountainous semi-arid regions of the globe.

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 (ETc _reg ) and Regional Net Irrigation Requirement (INET _reg ) estimated the Regional Gross Irrigation Water Demand (GIWD _reg ), 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 GIWD _reg in 2020 (11.3 × 10 ⁶ m ³ /year), driven by increased forage cultivation with crop water requirements (ET _c ) exceeding 1000 mm. That year also recorded the maximum SIWS (14.8 × 10 ⁶ m ³ /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.

Soil quality assessment, which considers numerous physical, chemical, and biological indicators, has long been a challenge for monitoring soil functions and ensuring sustainable resource use in agriculture. In this study, different indicator selection and weighting methods were compared to derive a reliable Soil Quality Index (SQI) in semi-arid agroecosystems. A total of 117 topsoil samples were taken from the Ap horizon within a 14,200 ha area of the Honam sub-catchment, southwestern Iran. Twenty-one soil indicators were measured and analyzed to assess the overall SQI. Soil indicator selection was performed using Principal Component Analysis (PCA), considering standard and norm value strategies, as well as component rotation. Four weighting approaches, including PCA, Coefficient of Variation (CV), correlation score (r), and Expert Opinion (EO), were applied to the Minimum Dataset (MDS) and Total Dataset (TDS) to compute the Integrated Quality Index (IQI), Nemoro (NQI), simple additive (IQIa), and Fuzzy Fertility Index (FFI). The performance of the SQI models was evaluated using the Sensitivity Index (SI) and their relationships with crop yield. The results showed that the combination of the norm value approach without component rotation was more effective in selecting the influential indicators for SQI determination. The Structural Stability Index (SSI), which integrates soil organic carbon and textural soil properties, was the key indicator with the highest contribution, ranging between 6.3% and 37.5% in most of the models. Among the evaluated approaches, the IQI-CV-MDS showed the highest sensitivity (SI = 6.8) and the strongest correlation (r = 0.53) with rainfed barley yield. The majority of the samples exhibited moderate SQI values, indicating a general risk of soil quality decline in the study area. The findings of this study highlight that appropriate indicator selection and weighting strategies are essential for improving the reliability of SQI assessments in semi-arid environments with diverse mountainous topography.