The negative impact of climate change is potentially damaging agroecosystem services that have constrained agricultural production and caused water scarcity in Central Asian countries, particularly in Uzbekistan. This study evaluates the efficiency of full (FDI) and deficit (DDI) drip irrigation regimes for amaranth (Amaranthus spp.) cultivation in the Tashkent region of Uzbekistan using the HYDRUS-1D simulation model. Field experiments were conducted over two growing seasons, accompanied by soil moisture monitoring, root zone analysis, and crop performance measurements while the accuracy of the obtained results was assessed against ground measured data. The results showed that compared to the FDI regime, amaranth under the DDI improved water productivity by 56.5% while exhibiting tolerance to water scarcity. The Pearson correlation analysis revealed a strong relationship between the simulated and observed SWC data for both irrigation regimes (R2 = 0.862 for FDI and R2 = 0.936 for DDI), indicating the model's predictive reliability. Although FDI produced higher yield (2004 kg/ha) over the two-year period, which was 25% (2 t ha-1) higher than the DDI regime (1,604 kg/ha). However, DDI demonstrated significantly greater water productivity (56.5% higher), attributed to reduced unproductive evaporation and the C4 nature of amaranth. Root system analysis revealed deeper penetration under DDI, suggesting adaptive responses to water stress. The findings of this study suggest that implementing precise irrigation technology in amaranth cultivation combined with the use of the HYDRUS-1D model in the context of inevitable climate change, can ensure the long-term sustainable management of water and land resources in arid regions.

Root-knot nematodes (RKN) cause extensive damage to grapevine cultivars. RKN-resistant grapevine rootstocks remain vulnerable to biotic and abiotic stresses. This study aimed to determine the influence of composted animal manures (CAMs) [chicken manure (CM), cow manure (CowM), and sheep manure (SM)] with or without plant growth-promoting rhizobacteria (PGPR) on the population of Meloidogyne incognita, free-living nematodes (FLNs) and predaceous nematodes (PNs) residing in the soils of vineyard cultivars (Flame, Superior and Prime). The nematodes were isolated from grapevine roots and rhizosphere soils, then the absolute frequency of occurrence (FO), relative FO, prominence value (PV), and population density (PD) were assessed. The impact of CAMs and PGPR on the growth parameters, fruit output, and quality of three grapevine varieties was subsequently evaluated. Eight treatments included a control without CAMs or PGPR amendments, the CAMs alone, or CAM treatments combined with PGPR. The results showed that FLNs and PNs were more abundant in Prime than Flame or Superior cultivars when poor sandy loam soils were supplied with CAMs. Among all tested manures, CM was the best treatment as a nematicide. This was evident from the decreased numbers of M. incognita and increased numbers of FLNs and PNs in grapevine fields. Compared to the soil-applied oxamyl (a systemic nematicide), which was efficiently suppressive on M. incognita for two months, CM significantly (P < 0.05) decreased PD of the phytonematodes for five months, improved soil structure and enhanced the soil biological activities. There were significant (P < 0.05) increases in the number of leaves/vines by 79.9, 78.8, and 73.1%; and total fruit weight/vine by 76.9, 75.0, and 73.0% in Flame, Superior, and Prime varieties, respectively, compared to untreated vines. Regardless of the cultivar, soils amended with CM + PGPR achieved the lowest number of M. incognita among all other treatments, followed by SM + PGPR and CowM + PGPR. It was concluded that CAMs amendment, mainly CM, along with PGPR in poor sandy soils of temperate areas, is considered a sustainable approach for reducing parasitic nematodes and improving agricultural management.

The application of biochar as a soil amendment has gained increasing attention due to its potential to improve soil properties, enhance plant growth, and mitigate environmental stresses. This study aims to evaluate the effects of different biochar treatments-wood biochar (WBc), vegetable biochar (VBc), and a mixture of wood and vegetable biochar (WVBc)-on the growth, physiological, and biochemical responses of Pisum sativum L. seedlings. A greenhouse experiment was conducted to evaluate the effects of biochar treatments-wood biochar (WBc), vegetable biochar (VBc), and a mixture of wood and vegetable biochar (WVBc)-on Pisum sativum L. seedlings. Seedlings were grown under controlled conditions, and various growth, physiological, and biochemical parameters were assessed, including plant biomass, photosynthetic efficiency, nutrient content, oxidative stress markers, and antioxidant defense responses. The findings revealed significant improvements across several plant growth metrics, including root and shoot lengths, fresh and dry biomass, with WVBc showing the most pronounced effects. Root length increased by 75.45%, shoot length by 32.4%, and shoot fresh weight by 43.4% compared to the control. Photosynthetic parameters also improved, with total chlorophyll content increasing by 50.1%, net photosynthetic rate by 28.3%, RWC by 17.0%, and WUE by 22.5% under WVBc treatment. Enhanced photosynthesis was attributed to higher nitrogen availability and improved soil moisture retention. Biochemical analyses indicated significant increases in total protein and carbohydrate content, with WVBc treatment yielding the highest gains. Additionally, glycine betaine (GB) production increased by 44.7%, while proline content decreased by 46.1%, suggesting improved stress tolerance. The reduction in oxidative stress markers (MDA and H2O2) further supports the role of biochar in mitigating oxidative damage. Moreover, biochar treatments enhanced the activities of key antioxidant enzymes and increased levels of non-enzymatic antioxidants such as reduced glutathione (GSH), ascorbic acid (AsA), and alpha-tocopherol, thereby boosting the plants' antioxidant defenses. The WVBc treatment significantly enhanced nutrient uptake, particularly nitrogen, potassium, and phosphorus, contributing to improved mineral content and plant health. Overall, this study highlights mixed wood-vegetable biochar (WVBc) as an effective soil amendment that enhances plant resilience, nutrient use efficiency, and crop productivity, offering a promising strategy for sustainable agriculture and stress mitigation.

Land degradation threatens environmental and agricultural development in the 21st century. To alleviate this problem, bench terracing has been implemented in eastern and southern Ethiopia. This paper investigates how farmers perceive the attributes and effectiveness of bench terracing in Ethiopia. A Multi-stage sampling techniques were applied to select 384 sample households. For this study, data were collected through primary and secondary sources, and the collected data were analyzed using descriptive statistics and content analysis methods. Primary data were collected using semi-structured questionnaires, focus groups, and key informant interviews; secondary data came from local authority reports. We found that bench terraces restored damaged land and improved crop yield where they were aptly implemented and maintained. The findings also disclose that 57.3% of farmers perceived that bench terracing was more cost-effective; 60.7% responded that it is compatible with the socio-cultural context; and 59.8% perceived Its outcomes are observable to others. However, when a farmer lacks sufficient social, human, or financial capital holdings and capabilities, it often fails. We conclude that the technology was adopted through a multifaceted process, promoted or hindered by both its attributes and effectiveness. Policy-makers and Planners should center those restraints on designing, implementing, and maintaining bench terracing. [GRAPHICS]

Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. This study utilized hyperspectral imaging at the leaf level to visualize drought stress in safflower plants ( Car- thamus tinctorius L.). Three safflower genotypes, Palenus, A82, and IL-111, were cultivated under three irrigation levels. Stress conditions were simulated by depleting 50%, 70%, and 90% of soil water content, representing unstressed (US), mild stress (MS), and severe stress (SS) conditions, respectively. Hyperspectral images of leaf samples were captured before any visible signs of water scarcity emerged. Classification analysis was performed using the full mean spectral data with partial least squares discriminant analysis, soft independent modeling of class analogy (SIMCA), support vector machines, and artificial neural network (ANN) classifiers. Feature selection methods were applied to extract the most informative wavebands, and ANN was used to build predictive models. Spatial analysis involved pixel-wise classification using both unsupervised (k-means clustering) and supervised (best classifiers) approaches. ANN outperformed other classifiers using the entire spectral data, effectively distinguishing US, MS, and SS classes in the Palenus, A82, and IL-111 genotypes, achieving F1-scores of 92.22%, 96.01%, and 96.47%, respectively. Among the feature selection methods, SIMCA-based features excelled in monitoring stress conditions in the Palenus and A82 genotypes. In supervised spatial analysis, ANN models clearly depicted the progression of stress in leaves across different genotypes. This study demonstrates the potential of hyperspectral imaging to differentiate various levels of drought stress in safflower, an important oilseed crop.

Drought is a perilous agrometeorological phenomenon that often causes crop damage in arid and semiarid regions vulnerable to climate variability. However, accurate drought monitoring remains deficient in many countries, including Kyrgyzstan, and the interconnections between several types of drought and contributions to crop yield are still unclear. Hence, we aimed to determine the propagation time in three types of drought (meteorological drought, soil drought, and vegetation drought) for understanding interconnections of them. Moreover, we focused on comprehensively evaluation the performance of multiple drought indices for each type over the complex terrain of Kyrgyzstan, especially for drought index of synergistic land surface temperature and vegetation conditions information. The results demonstrated that standard precipitation index (SPI) effectively detected meteorological drought, while the vegetation health index (VHI) coupled with temperature data was optimal for vegetation drought monitoring in Kyrgyzstan. Furthermore, our findings indicated a 1-month response time for soil drought at a 10 cm depth to SPI, and a 4-month response time at a 40 cm depth to meteorological drought (SPI). The response time of VHI to soil drought condition index (SMCI) was approximately 1 month, regardless of whether the soil drought occurred at a depth of 10 or 40 cm. In general, the response time of VHI to SPI was 3 months. Finally, by analyzing the correlation between crop yield productivity and drought indices, we discovered that the crop yield predictions by the three types of drought were differential and complex, but VHI was the most effective index. At the same time, VHIacc(May-Sep.), SMCIr(0-40 cm)_May-Sep., and SPI5_Aug. have different contributions to crop yield variations, and these are also differences in their impacts on different crops and provinces. The synergistic effect of the three types of drought may significantly improve crop yield prediction in Kyrgyzstan in future studies. These findings may significantly contribute to drought prevention and mitigation in drought-prone Central Asian countries.

Drought is a complicated and costly natural hazard and identification of critical drought factors is critical for modeling and forecasting of droughts and hence development of drought mitigation measures (the Standardized Precipitation-Evapotranspiration Index) in both space and time. Here we quantified relationships between drought and 23 drought factors using remote sensing data during the period of 2002-2016. Based on the Gradient Boosting Algorithm (GBM), we found that precipitation and soil moisture had relatively large contributions to droughts. During the growing season, the relative importance of Normalized Difference Water Index (NDWI-7) for SPEI3, SPEI6, SPEI9, and SPEI12 reached as high as 50%. However, during the non-growing season, the Snow Cover Fraction (SCF) had larger fractional relative importance for short-term droughts in the Inner Mongolia and the Loess Plateau which can reach as high as 10%. We also compared Extremely Randomized Trees (ERT), H2O based Deep Learning (Model developed by H2O.deep learning in R H2O.DL), and Extreme Learning Machine (ELM) for drought prediction at various time scales, and found that the ERT model had the highest prediction performance with R-2 > 0.72. Based on the Meta-Gaussian model, we quantified the probability of maize yield reduction in the North China Plain under different compound dry-hot conditions. Due to extreme drought and hot conditions, Shandong Province in North China had the highest probability of >80% of the maize yield reduction; due to the extreme hot conditions, Jiangsu Province in East China had the largest probability of >86% of the maize yield reduction. (C) 2021 Elsevier B.V. All rights reserved.

Under conditions of ongoing climate warming and increasing anthropogenic impact on land resources, the use of moisture-saving soil-protecting technologies becomes especially important. Snow cover is of great importance for agriculture, as it changes radiation and thermal balance of underlying surface, protecting soil from cooling and winter crops from freezing, accumulates winter precipitation, being the most important source of increasing soil moisture reserves in arid and subarid zones in spring. Winter precipitation accounts for up to 30% of the annual norm. Soil moisture reserves formed with their help take up to 42% in total water consumption for grain crop yield formation during wet summer and up to 75% during dry summer. This article aims to study the effect of different methods of snow retention and snow cover height on the yield of grain crops. An effective method of snow retention is leaving high stubble after harvesting winter and spring crops. Leaving stubble bushes with a height of 0.35-0.40 m and a width of 1.5 m every 4.5 m provides accumulation of a solid snow cover in steppe areas with a height of 0.30-0.35 m, which increases the yield of wheat. Waders provide a more uniform distribution of snow cover than forest strips. Climate change contributes to the fact that snow retention becomes an urgent problem not only in the dry steppe, but also to the north - even in the forest-steppe. Creation of snow retarders was done on Vetelny state farm, located in Balashovsky district in the western part of Saratov region, in the steppe zone, where chernozem soils prevail in the soil cover. In the autumn period, snow barriers were installed, and in the winter period, their effect on snow accumulation was studied. The study of the effect of snow barriers on soil moisture accumulation during the growing season of winter wheat was compared in the zones of dry steppe, steppe and forest-steppe. It was revealed that during regrowth of winter wheat the least amount of productive moisture stocks in 0-1.0 m soil layer was contained in dry steppe 1377 m(3).ha(-1), the highest in forest-steppe zone up to 1841 m(3).ha(-1). Snow retention increased the amount of moisture in the soil in the dry steppe, steppe and foreststeppe zone by 251, 151, 115-131 m(3).ha(-1), or 18, 10, 6-8%, respectively. Thus, rational use of winter precipitation is a significant reserve of agricultural landscape productivity increase, especially in dry-steppe areas.