The evolution of the major achievements in water lifting devices with emphasis on the major technologies over the centuries is presented and discussed. Valuable insights into ancient water lifting technologies with their apparent characteristics of durability, adaptability, and sustainability are provided. A comparison of the relevant technological developments in several early civilizations is carried out. These technologies are the underpinning of modern achievements in water engineering. They represent the best paradigm of probing the past and facing the future. A timeline of the historical development of water pumps worldwide through the last 5500 years of the history of mankind is presented. A chronological order is followed with emphasis on the major civilizations.

In this study, the ability of the seasonal autoregressive integrated moving average (SARIMA) and autoregressive integrated moving average (ARIMA) models was investigated for long-term runoff forecasting in the United States. In the first stage, the amount of runoff is forecasted for 2011 in each US state using the data from 1901 to 2010 (mean of all stations in each state). The results show that the accuracy of the SARIMA model is better than that of the ARIMA model. The relative error of the SARIMA model for all states is <5%. In the second stage, the runoff is forecasted for 2001 to 2011 by using the average annual runoff data from 1901 to 2000. The SARIMA model with periodic term equal to 20, R² = 0.91, and mean bias error (MBE) = 1.29 mm is the best model in this stage. According to the obtained results, a trend is observed between annual runoff data in the United States every 20 years or almost a quarter century.

In this study, annual precipitation was forecast by coding in MATLAB software environment based on a non‐linear autoregressive neural network (NARNN), non‐linear input–output (NIO) and NARNN with exogenous input (NARNNX). Historical precipitation data (27 precipitation gauge stations located in Gilan, Iran) were used as two 21 year sets from 1968 to 1988 and from 1989 to 2009 for calibration and testing of the networks, respectively. Results showed that the accuracy of the NARNNX was better than that of the NARNN and NIO, based on r values. However, performance of the networks was not satisfactory because the number of neurons in the hidden layer and the roles of training, validation and testing phases were lacking flexibility and change. Optimization of the number of neurons in the hidden layer and the determination of the best role among the different phases led to improvement of network accuracy. The r values were <0.73 only for five stations in the optimized NARNN and <0.74 only for those stations with optimized NIO.

This paper aims to estimate ratio of area equipped for irrigation to cultivated area (AI) in Africa in 2035 and 2060 using studies of agricultural water management from 1962 to 2011. For this purpose, all necessary information was gathered from Food and Agriculture Organization of the United Nations (FAO) and their values were checked using The World Bank Group (WBG). Among all presented data in the FAO database, 10 indices were selected (due to more importance and more availability for all the regions in Africa). The selected indices were analysed for all seven regions in the study area and the amount of AI was estimated by three different scenarios and using other nine indices. The results show that changes of AI are 0.3% to 49.5% and 16.5% to 83.2% from 2011 to 2035 and 2060, respectively. Indian Ocean Islands has a better potential to increase AI in the future. A considerable note is the change of irrigation status in the future than the current status. In 2011, AI of Sudano-Sahelian is more than AI of Indian Ocean Islands, however, in the future; AI of Sudano-Sahelian would be less than AI of Indian Ocean Islands based on all scenarios.

Food security will be a substantial issue in the near future due to the expeditiously growing global population. The current trend in the agriculture industry entails the extravagant use of synthesized pesticides and fertilizers, making sustainability a difficult challenge. Land degradation, lower production, and vulnerability to both abiotic and biotic stresses are problems caused by the usage of these pesticides and fertilizers. The major goal of sustainable agriculture is to ameliorate productivity and reduce pests and disease prevalence to such a degree that prevents large-scale damage to crops. Agriculture is a composite interrelation among plants, microbes, and soil. Plant microbes play a major role in growth promotion and improve soil fertility as well. Bacillus spp. produces an extensive range of bio-chemicals that assist in plant disease control, promote plant development, and make them suitable for agricultural uses. Bacillus spp. support plant growth by N fixation, P and K solubilization, and phytohormone synthesis, in addition to being the most propitious biocontrol agent. Moreover, Bacilli excrete extracellular metabolites, including antibiotics, lytic enzymes, and siderophores, and demonstrate antagonistic activity against phytopathogens. Bacillus spp. boosts plant resistance toward pathogens by inducing systemic resistance (ISR). The most effective microbial insecticide against insects and pests in agriculture is Bacillus thuringiensis (Bt). Additionally, the incorporation of toxin genes in genetically modified crops increases resistance to insects and pests. There is a constant increase in the identified Bacillus species as potential biocontrol agents. Moreover, they have been involved in the biosynthesis of metallic nanoparticles. The main objective of this review article is to display the uses and application of Bacillus specie as a promising biopesticide in sustainable agriculture. Bacillus spp. strains that are antagonistic and promote plant yield attributes could be valuable in developing novel formulations to lead the way toward sustainable agriculture.

In many regions in the world, more than 90% of areas equipped for irrigation apply surface methods. The major problem of the surface irrigation system is low performance due to poor design, operation, and maintenance. Use of the mathematical models for simulation of surface irrigation is necessary for reducing costs and decrease of time in analysis of indexes including application efficiency and distribution uniformity. This study aims to simulate border irrigation systems using the SIRMOD (surface irrigation simulation, evaluation and design, developed by Utah State University, Logan, UT, USA) software package under open- and closed-end conditions. For this purpose, 22 sets of data including four no-cultivated open-end borders, nine no-cultivated closed-end borders, and nine cultivated closed-end borders were used. The results showed that the models predicted open-end conditions better than closed-end for recession time. In addition, the hydrodynamic (HD) and the zero inertia (ZI) models estimated volume of infiltrated water, equal or less than volume of observed water in all the borders. Although the HD model uses the Saint-Venant equations without simplification, during numerical solution of them by the software, uncertainty is raised due to further calculations than the ZI and kinematic wave models. This leads to further error of the HD model than the other models in some cases.