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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The cultivation of Panax notoginseng has been plagued by a multitude of challenges, including recurrent diseases, suboptimal value, inadequate quality, and environmental degradation resulting from improper water and fertilizer management. To address these issues and improve the yield of P. notoginseng and its saponin content, this study endeavors to identify the optimal irrigation and fertilization levels in shaded environments in Yunnan Province in Southwest China. In this field experiment, three-year-old plants were tested to evaluate the effects of water, soluble organic fertilizers, and their combinations on plant growth, physiological parameters, yield, and saponin content. The experiment included 12 treatments with three types of irrigation (10 (W1), 15 (W2), and 20 (W3) mm), totaling 440, 660, and 880 mm, and four levels of the total amount of fertilization (F1 (60, total N 12.6, total P 5.5, and total K 10.5 kg ha−1), F2 (90, total N 18.9, total P 8.3, and total K 15.7 kg ha−1), F3 (120, total N 25.2, total P 11.0, and total K 20.9 kg ha−1), F4 (150, total N 31.5, total P 13.8, and total K 26.1 kg ha−1)). The randomized complete block design was used, with 36 plots in total and 3 replications. The study utilized the TOPSIS method to determine the most effective water and fertilizer management strategy for the growth and production of P. notoginseng. The assessment of yield, water and fertilizer productivity, and saponin content across all treatments revealed that the W3F3 treatment resulted in significant increases in the plant's height, stem diameter, and net photosynthetic rate. Meanwhile, the W2F3 treatment exhibited the best root morphological traits. The W3F4 treatment effectively increased dry matter and transpiration. The combination of water and fertilization had a coupling effect that not only increased yield to 1400 kg ha−1 but also improved water–fertilizer productivity. The application of the W2F3 treatment resulted in a significant increase in the accumulation of active components, leading to a total P. notoginseng saponin (PNS) content of 24.94%. Moreover, the comprehensive index obtained through the TOPSIS model indicated that the W2F3 treatment outperformed other treatments. Therefore, this treatment can be considered a promising water and fertilizer model for P. notoginseng cultivation, which can enhance its yield, quality, and productivity while promoting sustainable green development. [ABSTRACT FROM AUTHOR]

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 (FL: 360 kg·ha−1, FM: 720 kg·ha−1, and FH: 1080 kg·ha−1) and three types of coffee husk returning methods (CB: coffee husk biochar, CC: coffee husk compost, CA: 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 FM had the biggest effect on improving soil fertility, microorganisms, and enzyme activities compared with FL and FH. Moreover, compared to CA, CC significantly increased soil organic carbon, organic matter, and total nitrogen content. CC 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 FMCC 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 FMCC first. Therefore, FMCC 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. [ABSTRACT FROM AUTHOR]

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]