Crop water productivity assessment and planting structure optimization in typical arid irrigation district using dynamic Bayesian network.

Enhancing crop water productivity is crucial for regional water resource management and agricultural sustainability, particularly in arid regions. However, evaluating the spatial heterogeneity and temporal dynamics of crop water productivity in face of data limitations poses a challenge. In this study, we propose a framework that integrates remote sensing data, time series generative adversarial network (TimeGAN), dynamic Bayesian network (DBN), and optimization model to assess crop water productivity and optimize crop planting structure under limited water resources allocation in the Qira oasis. The results demonstrate that the combination of TimeGAN and DBN better improves the accuracy of the model for the dynamic prediction, particularly for short-term predictions with 4 years as the optimal timescale (R2 > 0.8). Based on the spatial distribution of crop suitability analysis, wheat and corn are most suitable for cultivation in the central and eastern parts of Qira oasis while cotton is unsuitable for planting in the western region. The walnuts and Chinese dates are mainly unsuitable in the southeastern part of the oasis. Maximizing crop water productivity while ensuring food security has led to increased acreage for cotton, Chinese dates and walnuts. Under the combined action of the five optimization objectives, the average increase of crop water productivity is 14.97%, and the average increase of ecological benefit is 3.61%, which is much higher than the growth rate of irrigation water consumption of cultivated land. It will produce a planting structure that relatively reduced irrigation water requirement of cultivated land and improved crop water productivity. This proposed framework can serve as an effective reference tool for decision-makers when determining future cropping plans.

[Characteristics of Soil Organic Carbon Components and Their Correlation with Other Soil Physical and Chemical Factors in Cotton Fields with Different Continuous Cropping Years in the Oasis on the Northern Edge of Tarim Basin].

The study of soil organic carbon components in continuous cropping cotton fields in oases is helpful to reveal the change characteristics of the soil organic carbon stability mechanism in arid areas under the effects of man-land relationships. In this study, the contents of soil organic carbon, easily oxidized organic carbon, dissolved organic carbon, and microbial biomass carbon in cotton fields with different continuous cropping years (2 a, 5 a, 12 a, 20 a, and 35 a) were collected and analyzed by using space instead of the time series method. Through redundancy analysis, the relationship between soil organic carbon components and other soil physical and chemical factors was discussed. The results showed that:① continuous cropping for different years had a significant impact on the content of soil organic carbon components in the study area. The contents of soil organic carbon, easily oxidized organic carbon, dissolved organic carbon, and microbial biomass carbon in continuous cropping cotton fields for 12 a, 20 a, and 35 a were higher than those in continuous cropping cotton fields and wasteland for 2 a and 5 a. ω(soil organic carbon) reached the peak value (7.06 g·kg-1) in the cotton field in 20 a, which was 76.91% higher than that in the wasteland. The content of soil organic carbon decreased with the deepening of the soil layer. ② Based on the redundancy analysis of soil organic carbon content and soil environmental factors, the results showed that the content of soil organic carbon was positively correlated with total nitrogen, available phosphorus, and water content and negatively correlated with pH value and bulk density. The importance of soil environmental factors on the interpretation of soil organic carbon content was as follows:total N>available P>pH value>bulk density>water content>available K>total salt.

Effects of Litter and Root Manipulations on Soil Bacterial and Fungal Community Structure and Function in a Schrenk's Spruce (Picea schrenkiana) Forest.

Soil microorganisms are the key driver of the geochemical cycle in forest ecosystem. Changes in litter and roots can affect soil microbial activities and nutrient cycling; however, the impact of this change on soil microbial community composition and function remain unclear. Here, we explored the effects of litter and root manipulations [control (CK), doubled litter input (DL), litter removal (NL), root exclusion (NR), and a combination of litter removal and root exclusion (NI)] on soil bacterial and fungal communities and functional groups during a 2-year field experiment, using illumina HiSeq sequencing coupled with the function prediction platform of PICRUSt and FUNGuild. Our results showed that litter and root removal decreased the diversity of soil bacteria and fungi (AEC, Shannon, and Chao1). The bacterial communities under different treatments were dominated by the phyla Proteobacteria, Acidobacteria, and Actinomycetes, and NL and NR reduced the relative abundance of the first two phyla. For the fungal communities, Basidiomycetes, Ascomycota, and Mortierellomycota were the dominant phyla. DL increased the relative abundance of Basidiomycetes, while NL and NR decreased the relative abundance of Ascomycota. We also found that litter and root manipulations altered the functional groups related to the metabolism of cofactors and vitamins, lipid metabolism, biosynthesis of other secondary metabolites, environmental adaptation, cell growth, and death. The functional groups including ectomycorrhizal, ectomycorrhizal-orchid mycorrhizal root-associated biotrophs and soil saprotrophs in the fungal community were also different among the different treatments. Soil organic carbon (SOC), pH, and soil water content are important factors driving changes in bacterial and fungal communities, respectively. Our results demonstrate that the changes in plant detritus altered the soil microbial community structure and function by affecting soil physicochemical factors, which provides important data for understanding the material cycle of forest ecosystems under global change.