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.

Rapeseed is one of China’s major oil crops, and accurate yield forecasting is crucial to the growth of the rapeseed industry and the country’s food security. In this study, the data on natural and socio-economic factors from 2001 to 2020 and the yield of rapeseed in China were used as the data basis. The Pearson correlation coefficient was used to analyze the relationship between the influencing factors and the yield of rapeseed, and the prediction effect of four machine learning models (linear regression (LR), decision tree (DTR), random forest (RF), and support vector machine (SVM)) on the yield of rapeseed was compared in China’s main rapeseed-producing area. The results demonstrate that the yield of rapeseed in China showed an increasing trend, but fluctuated greatly. Rural electricity consumption, gross agricultural production, the net amount of agricultural fertilizer application, effective irrigation area, total power of agricultural machinery, and consumption of agricultural plastic film had a positive effect on the increase in rapeseed yield. However, due to the impact of climate change and disasters, the yield of rapeseed has had significant fluctuations. A Pearson correlation analysis showed that socio-economic factors (rural electricity consumption, gross agricultural production, effective irrigation area, total power of agricultural machinery, consumption of agricultural plastic film, etc.) played a dominant role in rapeseed yield changes. The RF model had a good prediction effect on rapeseed yield, and natural factors and socio-economic factors had different effects on spring rapeseed and winter rapeseed. Winter rapeseed yield was mainly affected by socio-economic factors, accounting for as high as 89% of the importance. Among them, the sown area of rapeseed and the effective irrigation area had the greatest impact. The effects of natural factors and socio-economic factors on spring rapeseed yield were similar, accounting for 47% and 53%, respectively, and the mean annual precipitation, sunshine duration, and sown area of rapeseed were the most influential variables.

In order to explore water distribution in sprinkler irrigation systems under dynamic water pressure, an irrigation test was conducted in a sprinkler irrigation system equipped with the Hunter MP2000 full circular rotator ray sprinkler under “trapezoidal” waveform pressure to explore the influencing factors such as basic water pressure, dynamic pressure period, and the sprinkler combination spacing. The result shows that the basic pressure, sprinkler combination spacing, and their interaction significantly affected the coefficient of variation, average intensity, and coefficient of uniformity. The normalized comprehensive evaluation indexes were selected as the measurement standards. The optimal factor combination was found to be the basic pressure of 250 kPa, the dynamic pressure period of 200 s, and the sprinkler combination spacing of 6 m, and the corresponding evaluation index values were 0.08, 10.54 mm/h, and 93.20%, respectively. The uniformity coefficient was increased by 6.54% compared with the constant pressure of the same flow rate. Compared with the constant pressure of the same flow rate, the sprinkler area and the average intensity increased by 39.67% and decreased by 8.62% when the basic pressure was 250 kPa and the dynamic pressure period was 200 s. The average uniformity coefficient increased by 11.76% at the combined spacing of 6.0, 6.5, 7.0, 7.5, and 8.0 m. The results provide a theoretical basis for sprinkler irrigation decisions under dynamic pressure.

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.

This study uses information visualization analysis software to quantitatively analyze the literature characteristics and hotspot evolution in the field of research on the impact of climate change on cotton. The results showed that the number of literature was on the rise, and environmental sciences, agronomy, plant sciences, water resources and agriculture were the main disciplines in this field. Countries and regions such as China, the U.S.A, Argentina, Pakistan and India pay great attention to the research field, and the cooperation between countries was relatively closed. Many well-known institutions such as the Chinese Academy of Sciences, University of Agriculture Faisalabad, Nanjing Agricultural University and United States Department of Agriculture-Agricultural Research Service were committed to research in this field. ‘Yield’, ‘CO2’, ‘Adaptation strategy’, ‘Irrigation’, ‘Model’, etc., were the hot words in the research. Research hotspots had developed from the research on assessing the impact of climate change on future cotton production to focus on the impact of CO2 concentration on cotton production, coping strategies for cotton industry development under climate change and climate-smart agriculture issues.