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.

Amaranth (Amaranthus hypochondriacus L.) is a potential forage crop with a high yield and crude protein (CP) content; however, establishment methods need to improve for the crop to be less sensitive to typhoons. Optimal establishment, cultivation, and utilization in amaranth were examined in a variety of seasons and methods of establishment in 2021-2023. Four methods were examined: (1) direct seeding in rows, (2) direct seeding in spots, (3) soil seed balls, and (4) transplant pretreatment methods under a randomized blocked design (n = 3). Sowings every month from April to August were applied only in 2021, while establishments in April, May, and August with both pretreatment methods were applied in 2022 and 2023. The establishment in August successfully escaped damage from typhoons. The direct seeding of either rows or spots showed marginal success in establishment compared to stable establishment in pretreatment methods. In 2022 and 2023, the highest yield and CP content were achieved in soil seed balls plots in April and in both pretreated plots sown in August, respectively. The quality of silage fermentation showed a high pH, ranging from 4.52 to 6.39, due to the high CP content in 7.59-18.36% dry matter (DM). Sowing in April or August established with soil seed balls can avoid typhoon damage to have stable forage yields and can be processed with a favorable quality of amaranth silage in the region.

The effects of different concentrations of cadmium and 2,2 ',4,4 ',5,5 '-hexabrominated diphenyl ether (BDE-153) on the growth and related physiological and biochemical indexes of Amaranthus mangostanus L. (amaranth) were studied. The results showed that the presence of BDE-153 promoted the absorption of Cd by the amaranth and inhibited the migration of Cd from the roots to the shoots. At the same time, 0.1 mg/L of Cd had a synergistic effect on the migration of BDE-153, but 5 mg/L Cd inhibited the accumulation of BDE-153 in the aboveground part of the amaranth. In addition, the kinetics of the uptake of pollutants by the amaranth showed that both Cd and BDE-153 could be transported by amaranth, but Cd and BDE-153 were mainly enriched in the roots, and the presence of Cd may cause a lag in the uptake of BDE-153 in the shoots. Compared with the control group, the biomass of the amaranth affected by BDE-153 and a high concentration of Cd (5 mg/L) decreased by 30.2-49.5%, the chlorophyll content decreased by 43.0-60.3%, the Evans blue increased, and the MDA content was higher. The activities of superoxide dismutase (SOD) and catalase (CAT) also decreased with an increase in the BDE-153 concentration. This indicates that the interaction between BDE-153 and a high concentration of Cd (5 mg/L) is more toxic to amaranth than single Cd pollution. This paper provides the necessary data support for phytoremediation of heavy metal and organic compound pollution.

Amaranth (Amaranthus spp.) is a significant leafy vegetable and cereal crop with high nutrient benefits that is widely consumed worldwide. To maximise its yield, farmers massively rely upon synthetic fertilisers to enhance the quality of the crop. However, this obsessive usage of inorganic fertiliser leads to severe ecosystem damage. For agricultural and ecological sustainability, it is essential to comprehend the process underlying this environmental degradation. This paper analyses the effect of inorganic fertilisers on the growth and yield of Amaranthus. By identifying Amaranthus's productivity and adaptability in different chemically treated soil conditions and automatically phenotyping its traits using image-based deep learning models, this study aims to determine the overuse of synthetic fertilisers. A comparative evaluation of different state-of-art CNN models was carried out, and the experimental result proves that DenseNet-121 could be a more appropriate learning algorithm for the proposed system with 84% accuracy. It is believed that the proposed deep learning based automated phenotyping framework could greatly assist farmers in understanding the actual requirement of soil, thus avoiding the residual impact of fertiliser abuse in the environment.