Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R²) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.

Excessive use of chemical fertilizers deteriorates the soil environment and limits the normal growth of Arabica coffee trees. In order to identify the optimal coupling mode of chemical fertilizer application and biomass return that enhances the soil ecological environment and promotes the photosynthetic efficiency of Arabica coffee, this study investigated the impacts of three levels of inorganic fertilizers (F_L: 360 kg·ha⁻¹, F_M: 720 kg·ha⁻¹, and F_H: 1080 kg·ha⁻¹) and three types of coffee husk returning methods (C_B: coffee husk biochar, C_C: coffee husk compost, C_A: coffee husk ash) on the soil fertility, microbial amount, enzyme activity, and photosynthetic characteristics of the Arabica coffee root zone. The entropy weight-TOPSIS method was employed to evaluate the comprehensive benefits. The results showed that F_M had the biggest effect on improving soil fertility, microorganisms, and enzyme activities compared with F_L and F_H. Moreover, compared to C_A, C_C significantly increased soil organic carbon, organic matter, and total nitrogen content. C_C significantly enhanced the activities of soil phosphatase and urease, respectively, by 29.84% and 96.00%, and significantly increased the amount of bacteria, fungi, and actinomycetes by 62.15%, 68.42%, and 46.21%, respectively. The net photosynthetic rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs) of F_MC_C were significantly higher than those of other treatments. The comprehensive benefit evaluation of the soil environment and photosynthetic characteristics by the entropy weight-TOPSIS method ranked F_MC_C first. Therefore, F_MC_C was the optimal coupling mode for fertilizer application and the coffee husk returning method. The findings of this study not only provide scientific guidance for fertilizing Arabica coffee but also clarify the proper approach to returning coffee husk to the field, thereby improving soil ecology and promoting green and efficient production of specialty crops.

China has the largest apple-growing area and fresh fruit production in the world; however, water shortages and low fertilizer utilization rates have restricted agricultural development. It is a major challenge to obtain scientific and reasonable irrigation and fertilization systems for young apple trees in semi-arid regions of northern China. A 2-year field bucket experiment with four irrigation levels of W1 (75–90% Fs, where Fs is the field water holding capacity), W2 (65–80% Fs), W3 (55–70% Fs), and W4 (45–60% Fs), and three fertilizer levels of F1 (27-9-9 g N-P₂O₅-K₂O), F2 (18-9-9 g N-P₂O₅-K₂O), and F3 (9-9-9 g N-P₂O₅-K₂O) was conducted in 2019 and 2020, so as to explore the effects of different water and fertilizer treatments on the growth and physiological characteristics of young apple trees. The results showed that the plant growth, leaf area, and dry matter of young apple trees at each growing period reached maximum values under F1W2, and they showed a positive linear relationship with relative chlorophyll content (SPAD), net photosynthetic rate (Pn), transpiration rate (Tr), stomatal conductance (Gs), water consumption, and water use efficiency (WUE). With the growth of young apple trees, water-fertilizer coupling could significantly increase the leaf SPAD of young apple trees. Pn, Tr, and Gs reached the maximum value under F1W1, and although they decreased under F1W2, the water use efficiency increased by 2.3–25.7% and 4.0–23.8% under F1W2 compared with other treatments in two years, respectively. The water consumption of young apple trees increased with the increase of irrigation and fertilizer, and both dry matter and water productivity reached the maximum value under F1W2, which increased by 0.8%, 14.6% in 2019, and 0.6%, 11.1% in 2020 compared with F1W1, while water consumption decreased by 12.2% and 9.4% in both years. In conclusion, F1W2 treatment (soil moisture was controlled at 65–80% of field water holding capacity, and N-P₂O₅-K₂O was controlled at 27-9-9 g) was the best coupling mode of water and fertilizer for young apple trees in semi-arid areas of northern China.

In dryland agriculture, mulching methods and nitrogen application have been extensively adopted to improve water and nitrogen use efficiency and increase crop yield. However, there has been a scarcity of research on the combined effects of mulching types and nitrogen application on the growth and yield of soybean (Glycine max L.). In the present study, four nitrogen levels (N0: 0 kg N ha⁻¹, N1: 60 kg N ha⁻¹, N2: 120 kg N ha⁻¹, N3: 180 kg N ha⁻¹) and four mulching methods (NM: no mulching, SM: straw mulching, FM: film mulching, SFM: straw and film mulching) were set so as to evaluate the effects of mulching methods and nitrogen application on dry matter accumulation, grain yield, water-nitrogen use efficiency, and economic benefits of soybean in Northwest China from 2021 to 2022. The results show that the dry matter accumulation, yield formation, water and nitrogen use efficiency, and economic benefits of soybean were improved under different mulching methods (SM, FM, and SFM) and nitrogen applications (N1-N3), and that the effect is the best when the nitrogen application rate is N2 and the mulching method is FM. As such, a conclusion could be drawn that suitable nitrogen application (120 kg ha⁻¹) combined with film mulching was beneficial for the utilization of rainwater resources and soybean production in the dryland of Northwest China.

Groundwater overexploitation in West Ordos necessitates sustainable irrigation practices. This study evaluated three irrigation levels—W1 (3300 m³ · ha⁻¹), W2 (2850 m³ · ha⁻¹), and W3 (2400 m³ · ha⁻¹)—by modifying the wide-width planting pattern of maize. Additionally, two levels of straw mulch were analyzed: F1 (9000 kg · ha⁻¹) and F2 (no mulch). The study aimed to investigate the effects of these treatments on corn growth dynamics, soil water temperature, soil enzyme activity, yield, grain quality, and water use efficiency. The results indicated a decline in growth indices, enzyme activities, grain quality, and yield under the limited irrigation levels W2 and W3 compared to W1. The highest corn yields were observed with W1F1 (6642.54 kg · ha⁻¹) and W2F1 (6602.38 kg · ha⁻¹), with the latter showing only a 0.6% decrease. Notably, water use efficiency in the W2F1 treatment improved by 4.69%, 12.08%, 10.27%, 12.59%, and 12.96% compared to W1F1, W3F1, W1F2, W2F2, and W3F2, respectively. Straw mulch (F1) significantly elevated the soil temperature, increasing the effective accumulated temperature during the growth period by 10.11~85.79 °C, and boosted the soil enzyme activity by 10–25%. Under limited irrigation, the W2 (2850 m³ · ha⁻¹) and F1 (9000 kg · ha⁻¹ straw) treatments achieved the highest water productivity of 2.48 kg·m⁻³, maintaining a high yield of 6602.38 kg · ha⁻¹ while preserving nutrients essential to the corn’s quality. This approach presents a viable strategy for wide-width corn planting in groundwater-depleted regions, offering a scientifically grounded and sustainable water management solution for efficient corn production in West Ordos.

Biological soil crusts (BSCs) are often referred to as the “living skin” of arid regions worldwide. Yet, the combined impact of BSCs on soil carbon (C), nitrogen (N), phosphorus (P), and enzyme activities remains not fully understood. This study identified, screened and reviewed 71 out of 2856 literature sources to assess the responses of soil C, N, P and enzyme activity to BSCs through a meta-analysis. The results indicated that BSC presence significantly increased soil C, N, P and soil enzyme activity, and this increasing effect was significantly influenced by the types of BSCs. Results from the overall effect showed that soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), and available phosphorus (AP) increased by 107.88%, 84.52%, 45.43%, 27.46%, and 54.71%, respectively, and four soil enzyme activities (Alkaline Phosphatase, Cellulase, Sucrase, and Urease) increased by 93.65–229.27%. The highest increases in SOC, TN and AN content occurred in the soil covered with lichen crusts and moss crusts, and significant increases in Alkaline Phosphatase and Cellulase were observed in the soil covered with moss crusts and mixed crusts, suggesting that moss crusts can synergistically enhance soil C and N pool and enzyme activity. Additionally, variations in soil C, N, P content, and enzyme activity were observed under different environmental settings, with more pronounced improvements seen in coarse and medium-textured soils compared to fine-textured soils, particularly at a depth of 5 cm from the soil surface. BSCs in desert ecosystems showed more significant increases in SOC, TN, AN, and Alkaline Phosphatase compared to forest and grassland ecosystems. Specifically, BSCs at low altitude (≤500 m) with an annual average rainfall of 0–400 mm and an annual average temperature ≤ 10 °C were the most conducive to improving soil C, N, and P levels. Our results highlight the role of BSCs and their type in increasing soil C, N, P and enzyme activities, with these effects significantly impacted by soil texture, ecosystem type, and climatic conditions. The implications of these findings are crucial for soil enhancement, ecosystem revitalization, windbreak, and sand stabilization efforts in the drylands of China.