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The growth of root system directly affects the absorption and utilization of soil water and nitrogen, and understanding the responses of root characteristics to water and nitrogen regimes is thus crucial for optimizing water and nitrogen management. The root characteristics of each soil layer, i.e., root length, root surface area, and root volume, as well as fruit yield and water use efficiency of greenhouse tomato under drip fertigation in response to different irrigation levels and nitrogen rates were explored in northwest China. There were four irrigation levels, i.e., 50% ETC (W1), 75% ETC (W2), 100% ETC (W3), and 125% ETC (W4), where ETC is the crop evapotranspiration, and four nitrogen rates, i.e., 0 kg ha−1 (N1), 150 kg ha−1 (N2), 250 kg ha−1 (N3), and 350 kg ha−1 (N4). The results showed that reasonable irrigation and nitrogen regimes (W3N3) significantly increased fruit yield by 31.64% and root length, root surface area, and root volume by 45.03%, 61.24%, and 148.21% compare to W3N1, respectively. The promoting effect of increasing irrigation level on root characteristics increased with soil depth and had the greatest increases in root volume by 27.07%, 123.43%, and 211.47% for the 0–10, 10–20, and 20–30 cm soil layers, respectively. In addition, reducing irrigation level significantly increased the percentages of roots in the top soil by 29.71%, 26.77%, and 18.53% for root length, root surface area, and root volume, respectively. The reasonable nitrogen rate (N3) significantly increased fruit yield by 41.11%, water use efficiency by 34.42%, and root length, root surface area, and root volume by 40.42%, 41.44%, and 112.76%, respectively. The over-application of nitrogen (N4) reduced root characteristics of all soil layers, fruit yield, and water use efficiency. The promoting effect of increasing nitrogen rate on root length of each soil layer decreased with soil depth, by 71.01%, 48.96%, and 15.71% for 0–10, 10–20, and 20–30 cm soil layers, respectively. Irrigation level was the main factor dominating the root growth of each soil layer. The correlation analysis showed that fruit yield had significantly positive correlations with root characteristics in all soil layers, while water use efficiency had significantly positive correlations with the percentages of root length and root surface area in the 0–10 cm soil layer. In conclusion, rational water and nitrogen regimes achieved better fruit yield by promoting root growth of greenhouse tomato, and the water use efficiency of greenhouse tomato was improved by increasing the root percentage in the topsoil layer to alleviate the adverse effects under water stress conditions. This study reveals how irrigation volume and nitrogen application can enhance tomato yield and water use efficiency by regulating root characteristics and vertical root distribution, providing support for understanding the response of root systems to changes in soil water and nitrogen conditions.

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 21 to 27, show the highest correlation with winter wheat chlorophyll content at a scale of 26, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 26 and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 26 processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R2) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture.

Applying hyperspectral remote sensing technology to the prediction of soil moisture content (SMC) during the growth stage of soybean emerges as an effective approach, imperative for advancing the development of modern precision agriculture. This investigation focuses on SMC during the flowering stage under varying nitrogen application levels and film mulching treatments. The soybean canopy’s original hyperspectral data, acquired at the flowering stage, underwent 0–2-order differential transformation (with a step size of 0.5). Five spectral indices exhibiting the highest correlation with SMC were identified as optimal inputs. Three machine learning methods, namely support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN), were employed to formulate the SMC prediction model. The results indicate the following: (1) The correlation between the optimal spectral index of each order, obtained after fractional differential transformation, and SMC significantly improved compared to the original hyperspectral reflectance data. The average correlation coefficient between each spectral index and SMC under the 1.5-order treatment was 0.380% higher than that of the original spectral index, with mNDI showing the highest correlation coefficient at 0.766. (2) In instances of utilizing the same modeling method with different input variables, the SMC prediction model’s accuracy follows the order: 1.5 order > 2.0 order > 1.0 order > 0.5 order > original order. Conversely, with consistent input variables and a change in the modeling method, the accuracy order becomes RF > SVM > BPNN. When comprehensively assessing model evaluation indicators, the 1.5-order differential method and RF method emerge as the preferred order differential method and model construction method, respectively. The R2 for the optimal SMC estimation model in the modeling set and validation set were 0.912 and 0.792, RMSEs were 0.005 and 0.004, and MREs were 2.390% and 2.380%, respectively. This study lays the groundwork for future applications of hyperspectral remote sensing technology in developing soil moisture content estimation models for various crop growth stages and sparks discussions on enhancing the accuracy of these different soil moisture content estimation models.

Economic benefits and risk premiums significantly affect the production system decision making of farmers and government departments. This study evaluated the economic feasibility and estimated the risk premium of 12 rainfed soybean production systems with various planting densities, fertilization rates and planting patterns by considering the impact of soybean price fluctuation. There were two planting densities (D1: 160,000 plants ha−1 and D2: 320,000 plants ha−1), two fertilization rates (F1: 20 kg ha−1 N, 30 kg ha−1 P, 30 kg ha−1 K; F2: 40 kg ha−1 N, 60 kg ha−1 P, 60 kg ha−1 K) and three planting patterns (F+W0: flat cultivation with no irrigation; R+W0: plastic-mulched ridge-furrow cultivation (PMRF) with no irrigation; R+W1: PMRF with supplemental irrigation of 30 mm at the pod-filling stage). Based on the two-year (2019–2020) field data in a semi-humid drought-prone region of northwest China and soybean price fluctuation from January 2014 to June 2021, the net income (NI) was calculated by considering the impact of soybean price fluctuation and assuming constant soybean production costs. The net present value (NPV) method and the stochastic efficiency with respect to a function (SERF) method were used to evaluate the profitability of protective alternatives and the risk of these alternatives. The results showed that the 12 proposed soybean production systems were economically feasible. Reducing the fertilization rate reduced the input costs, but it did not necessarily result in a decrease in soybean yield and NI. The payback period of all production systems was within two years for farmers investing through loans. High-fertilizer and high-density production systems made personal investment obtain the highest economic benefit in this study, which was not the best investment strategy from the perspective of production-to-investment ratio and environmental protection departments. The preferences of farmers with various risk aversion and environmental protection departments in terms of risk premium were also proposed. The economic and risk assessment framework of this study can enhance the understanding of the adjustment of production systems from different perspectives, and provide strategies for promoting the protection of economic, environmental and socially sustainable agricultural systems.

Biochar is a potential solution for addressing environmental problems related to excessive nitrogen (N). However, there is still some debate about the absorption and desorption of nitrate nitrogen (NO3−-N). Therefore, this study investigated the NO3−-N adsorption and desorption performance onto biochar and biochar-soil mixture to address this gap. The results showed that the biochar produced from apple branches had the ability to absorb NO3−-N with an absorption capacity of 3.51 mg·g−1. The absorption data fitted well with the pseudo-second-order kinetic model and Langmuir model. The application of biochar significantly improved soil absorption capacity and slow release of NO3−-N. While higher NO3−-N concentrations had better NO3−-N supply capacity and poorer slow-release effect. Integrating nutrient supply and slow-release effect, it is recommended to control the application ratio of biochar to NO3−-N at 34–42.75 g·g−1. Although the unoptimized biochar application rate cannot be directly applied to the soil as a slow-release fertilizer carrier to meet commercial standards, biochar modification provides new possibilities for this purpose. Moreover, compared with traditional slow-release fertilizer, biochar had good stability and regeneration performance, alleviating the high cost due to the biochar price. In general, biochar still has potential and prospects as a slow-release material. This study provides support for biochar in mitigating environmental problems associated with excess N.

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.

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.

Understanding the spatial distributions of soil water, temperature and nutrients as well as their effects on maize growth and grain yield is vital for optimizing drip fertigation regimes. In this study, a 2 year field experiment was conducted on drip-fertigated spring maize with plastic mulching in arid northwestern China in 2015 and 2016. Four irrigation levels were set: as I60 (60% ETc; ETc is crop evapotranspiration), I75 (75% ETc), I90 (90% ETc) and I105 (105% ETc) in 2015; and as I60 (60% ETc), I80 (80% ETc), I100 (100% ETc) and I120 (120% ETc) in 2016. Two fertilization rates of N-P2O5-K2O were set: as F180 (180-90-90) and F240 (240-120-120). The results showed that the average soil water content in the deeper soil layer (80–120 cm) increased with the increase in irrigation level, and the lowest average soil water content in the 0–80 cm soil layer occurred under I95 in 2015 and under I100 in 2016. The irrigation level more significantly influenced the soil temperature at 5 cm than at the other depths. With the decrease in the irrigation level and progression of the growth period, the soil temperature increased. The soil nitrate nitrogen content in the root zone decreased with increasing irrigation level. The largest soil nitrate nitrogen content at the 0–100 cm depth occurred under I60 in both 2015 and 2016. Significant differences were observed for root length density in the 0–20 cm soil layer at various lateral locations. In deeper (60–100 cm) soil layers, the root length density under I75 (2015) and I80 (2016) was greater than at other depths. Grain yield, water use efficiency (WUE) and partial factor productivity (PFP) increased with the increase in irrigation level in 2015, while it increased and then decreased in 2016. I105F180 achieved the maximum grain yield (18.81 t ha−1), WUE (3.32 kg m−3), and PFP (52.26 kg kg−1) in 2015, while I100F180 achieved the maximum grain yield (20.51 t ha−1), WUE (3.99 kg m−3), and PFP (57.02 kg kg−1) in 2016. The optimal drip fertigation regimes for spring maize in arid northwest China were recommended as 90–100% ETc and 180-90-90 (N-P2O5-K2O) kg hm−2.

Leaf area index (LAI) and above-ground biomass are both vital indicators for evaluating crop growth and development, while rapid and non-destructive estimation of crop LAI and above-ground biomass is of considerable significance for crop field management. Owing to the advantages of repeatable and high-throughput observations, spectral technology provides a feasible method for obtaining LAI and above-ground biomass of crops. In the present study, the spectral, LAI and above-ground biomass data of winter wheat were collected, and 7 species (14 in total) were calculated based on the original and first-order differential spectrum correlation spectral indices with LAI. Then, the correlation matrix method was used for correlation with LAI. The optimal wavelength combination was extracted, and the results were calculated as the optimal spectral index related to LAI. The calculation process of the optimal spectral index related to above-ground biomass was the same as that aforementioned. Finally, the optimal spectral index was divided into three groups of model input variables, winter wheat LAI and above-ground biomass estimation models were constructed using support vector machine (SVM), random forest (RF) and a back propagation neural network (BPNN), and the models were verified. The results show that the correlation coefficient between the highest of the optimal spectral indices, the LAI, and the above-ground biomass of winter wheat exceeded 0.6, and the correlation was good. The methods for establishing the optimal estimation models for LAI and above-ground biomass of winter wheat are all modeling methods in which the input variables are the combination of the first-order differential spectral index (combination 2) and RF. The R2 of the LAI estimation model validation set was 0.830, the RMSE was 0.276, and the MRE was 6.920; the R2 of the above-ground biomass estimation model validation set was 0.682, RMSE was 235.016, MRE was 4.336, and the accuracies of both models were high. The present research results can provide a theoretical basis for crop monitoring based on spectral technology and provide an application reference for the rapid estimation of crop growth parameters.

This study explored the changes in the photosynthetic characteristics of the Fengdan peony under high-temperature stress to provide a reference for understanding the tolerance of peony plants under heat stress. In this study, the effects of high-temperature stress (40 °C) on the photosynthetic characteristics of the Fengdan leaves were studied. At 25 °C, the net photosynthetic rate (Pn), stomatal conductance (Gs), and transpiration rate (Tr) of the leaves decreased gradually with the increase in heat stress time, and intercellular CO2 concentration (Ci) decreased first and then increased. High-temperature stress reduced the light energy absorption (ABS/RC) and capture (TRO/RC), light energy for electron transport (ETO/RC), and heat dissipation (DIO/RC) per unit leaf area. The maximum photochemical efficiency (FV/FM), leaf photochemical performance index (PIabs), the probability that captured excitons can transfer electrons to other electron acceptors in the electron transport chain beyond QA (ψO), and the quantum yield for electron transport (φEo), decreased gradually. The results showed that high temperatures damaged the photosynthetic capacity of the peony leaves and destroyed the photosynthetic apparatus of leaves. This study provides a reference for understanding the photosynthetic characteristics and tolerance of peony plants under heat stress.

In order to understand the distribution, occurrence forms, and influencing factors of chromium (Cr), nickel (Ni), and copper (Cu) in soil aggregates, a five-step extraction method was used to determine their forms in soil aggregates of different sizes in a mountainous area of northern Hebei Province. The ecological risk was evaluated using the geo-accumulation index (Igeo) and primary and secondary comparison value method (RSP). Redundancy analysis (RDA) was used to identify the main factors affecting the distribution and morphology of Cr, Ni, and Cu in soil. The results showed that in vertical distribution, Cr, Ni, and Cu were concentrated in the surface soil, but there was no clear relationship between soil depth and heavy metal content. The distribution characteristics revealed that Cr, Ni, and Cu in soils mainly existed in relatively stable Fe-Mn oxides and residue states, and their morphology in aggregates did not vary considerably with particle size. Furthermore, the RSP results showed that the pollution risk of Cr, Ni, and Cu was higher, with Cr and Ni posing the highest risk in the 0.5–1 mm and 1–2 mm particle size ranges. The RDA results showed that available phosphorus and soil organic matter (SOM) were the main factors that caused the characteristic difference of 1–2 mm aggregate components. Additionally, hydrolyzed nitrogen, cation exchange capacity (CEC), and calcium exchange have positive effects on the residual state of Cr. For Ni, SOM, CEC and exchangeable calcium have positive effects on the binding state of Fe and Mn oxides and carbonate. For Cu, CEC and exchangeable calcium are the key factors that cause the morphological differences of aggregates. Based on the above results, a theoretical basis has been provided for the prevention and control of pollution in the subsequent research area.