γ-polyglutamic acid (γ-PGA), as an environmentally sustainable material, is extensive applied in agriculture for enhancing water and fertilizer utilization efficiency, augmenting crop yield, and ameliorating soil conditions. However, the effect of γ-PGA in conjunction with sesame cake fertilizer on the soil environment remains uncertain.The aim of this study is to investigate the effect of γ-PGA on soil nutrients, water use efficiency (WUE) and nitrogen use efficiency (NUE), and maize yield across various levels of sesame cake fertilizer. Additionally, the study seeks to identify the optimal ratio to establish a theoretical and practical foundation for sustainable agricultural development and the promotion of ecological agriculture. Through field experiments, nine treatments were established, comprising three levels of sesame cake fertilizer application rates (B1 = 900 kg/hm² for low fertility, B2 = 1100 kg/hm² for medium fertility, and B3 = 1300 kg/hm² for high fertility) and three levels of γ-PGA application rates (R1 = 200 kg/hm², R2 = 400 kg/hm², and R3 = 600 kg/hm²). The results can be outlined as follows: (1) When γ-PGA application rate increased, total nitrogen (TN) exhibited a synergistic effect under B1 treatment, but an antagonistic effect under B2 and B3 treatments. At the 6-leaf stage (V6), 12-leaf stage (V12), and tasseling stage (VT), available phosphorus (AP) exhibited antagonistic effects. However, at the filling stage (R2) and maturity stage (R6), AP in B1 and B2 treatments at various depths underwent partial transformation into a synergistic effect. The levels of available potassium exhibited a notable antagonistic effect, leading to a decrease in harvest index (HI). B2 treatment demonstrated superior results compared to the B1 and B3 treatments, with the highest levels observed under B2R1 treatment; (2) TN content in the 0–40 cm soil layer increased during the filling period, and it was uniformly distributed in the 40–60 cm soil layer. When the soil AP was located in the 0–60 cm soil layer, there was an increase in AP content during the mature period. Following the tasseling period, different treatments exhibited varying patterns of increase in response to the presence of potassium within the 0–60 cm soil layer. Consequently, in cases where the sesame cake fertilizer content is low, the interaction between γ-PGA can compensate for the deficiency of fertilizer, thereby enhancing water and nitrogen utilization efficiency. The optimal fertilization strategy for enhancing soil nutrient distribution, WUE and NUE, and yield is proposed to be the application of 1100 kg/hm² sesame cake fertilizer and 200 kg/hm² γ-PGA.

The joint flood control operation of reservoir groups is a complex engineering problem with a large number of constraints and interdependent decision variables. Its solution has the characteristics of strong constraint, multi-stage, nonlinearity, and high dimension. In order to solve this problem, this paper proposes a hybrid slime mold and arithmetic optimization algorithm (HSMAAOA) combining stochastic reverse learning. Since ancient times, harnessing the Yellow River has been a major event for the Chinese nation to rejuvenate the country and secure the country. Today, flood risk is still the greatest threat to the Yellow River basin. This paper chooses five reservoirs in the middle and lower reaches of the Yellow River as the research object, takes the water level of each reservoir in each period as the decision variable, and takes the peak clipping of Huayuankou control point as the objective to build an optimization model. Then, HSMAAOA is used to solve the problem, and the results are compared with those of the slime mold algorithm (SMA) and particle swarm optimization (PSO). The peak clipping rates of the three algorithms are 52.9% (HSMAAOA), 48.69% (SMA), and 47.55% (PSO), respectively. The results show that the HSMAAOA algorithm is better than other algorithms. This paper provides a new idea to solve the problem of the optimal operation of reservoir flood controls.

The optimal operation of reservoir groups is a strongly constrained, multi-stage, and high-dimensional optimization problem. In response to this issue, this article couples the standard Pelican optimization algorithm with adaptive ε constraint methods, and further improves the optimization performance of the algorithm by initializing the population with a good point set, reverse differential evolution, and optimal individual t-distribution perturbation strategy. Based on this, an improved Pelican algorithm coupled with adaptive ε constraint method is proposed (ε-IPOA). The performance of the algorithm was tested through 24 constraint testing functions to find the optimal ability and solve constraint optimization problems. The results showed that the algorithm has strong optimization ability and stable performance. In this paper, we select Sanmenxia and Xiaolangdi reservoirs as the research objects, establish the maximum peak-cutting model of terrace reservoirs, apply the ε-IPOA algorithm to solve the model, and compare it with the ε-POA (Pelican algorithm coupled with adaptive ε constraint method) and ε-DE (Differential Evolution Algorithm) algorithms, the results indicate that ε. The peak flow rate of the Huayuankou control point solved by the IPOA algorithm is 12,319 m³/s, which is much lower than the safe overflow flow rate of 22,000 m³/s at the Huayuankou control point, with a peak shaving rate of 44%, and other algorithms do not find effective solutions meeting the constraint conditions. This paper provides a new idea for solving the problem of flood control optimal operation of cascade reservoirs.

The optimal control problem of reservoir group flood control is a complex, nonlinear, high-dimensional, multi-peak extremum problem with many complex constraints and interdependent decision variables. The traditional algorithm is slow and easily falls into the local optimum when solving the problem of the flood control optimization of reservoir groups. The intelligent algorithm has the characteristics of fast computing speed and strong searching ability, which can make up for the shortcomings of the traditional algorithm. In this study, the improved sparrow algorithm (ISSA) combining Cauchy mutation and reverse learning strategy is used to solve the flood control optimization problem of reservoir groups. This study takes Sanmenxia Reservoir and Xiaolangdi Reservoir on the mainstream of the Yellow River as the research object and Huayuankou as the downstream control point to establish a joint flood control optimization operation model of cascade reservoirs. The results of the improved sparrow algorithm (ISSA), particle swarm optimization (POS) and sparrow algorithm (SSA) are compared and analyzed. The results show that when the improved ISSA algorithm is used to solve the problem, the maximum flood peak flow of the garden entrance control point is 11,676.3 m³, and the peak cutting rate is 48%. The optimization effect is obviously better than the other two algorithms. This study provides a new and effective way to solve the problem of flood control optimization of reservoir groups.

With the constant advancement of irrigation technology and the continuous expansion of irrigation areas, non-point source pollution (NPS) caused by agricultural activities has posed a persistent threat to ecosystems and biological safety. Against this backdrop, it is imperative to lay scientific foundations for green, sustainable, and high-quality agricultural development through a thorough review of the relevant research progress. In this study, bibliometric methods are adopted to comprehensively analyze and visualize the current state and key literature on agricultural irrigation and NPS pollution from 2010 to July 2024. The focus of this study is specifically on summarizing the research hotspots and development trends of different irrigation methods and the mechanisms behind their impacts on NPS pollution. The results indicate that publications from the United States and China account for 63.8% of the total, but the fragmentation of research efforts remains, suggesting a necessity to strengthen international and regional collaboration. There are three institutions with the highest publication output, namely Northwest A&F University, Hohai University, and the Chinese Academy of Sciences. The subjects identified as the key areas of research on irrigation-related NPS pollution (IRR-NPS) include precision irrigation, rapid water pollution response, spatiotemporal management, interdisciplinary integration, wastewater treatment, and crop models. Regarding future research, it is necessary to focus attention on real-time precision irrigation, standardized crop models, data accuracy, spatiotemporal pollution coordination, pollution purification technology development, interdisciplinary integrated governance, and the innovative applications of soil improvement technologies. In addition to offering theoretical support and practical guidance for the management of agricultural NPS pollution, this study also provides management and technical support for policymakers, which is beneficial for advancing agricultural irrigation technology and environmental preservation.