Agriculture faces the challenge of feeding a growing population with limited or depleting fresh water resources. Advances in irrigation systems and technologies allow site-specific application of irrigation water within the field to improve water use efficiency or reduce water usage for sustainable crop production, especially in arid and semi-arid regions. This paper discusses recent development of variable-rate irrigation (VRI) technologies, data and information for VRI application, and impacts of VRI, including profitability using this technology, with a focus on agronomic factors in precision water management. The development in sprinkler systems enabled irrigation application with greater precision at the scale of individual nozzle control. Further research is required to evaluate VRI prescription maps integrating different soil and crop characteristics in different environments. On-farm trials and whole-field studies are needed to provide support information for practical VRI applications. Future research also needs to address the adjustment of the spatial distribution of prescription zones in response to temporal variability in soil water status and crop growing conditions, which can be evaluated by incorporating remote and proximal sensing data. Comprehensive decision support tools are required to help the user decide where to apply how much irrigation water at different crop growth stages to optimize water use and crop production based on the regional climate conditions and cropping systems.

The excessive consumption of herbicides has gradually led to the herbicide resistance weed phenomenon. Managing herbicide resistance weeds can only be explicated by applying high-tech strategies such as artificial intelligence (AI)-based methods. We review here AI-based methods and tools against herbicide-resistant weeds. There are a few commercially available AI-based tools and technologies for controlling weed, as machine learning makes the classification process significantly easy, namely remote sensing, robotics, and spectral analysis. Although AI-based techniques make outstanding improvements against herbicide resistance weeds, there are still limited applications compared to the real potential of the methods due to the challenges. In this review, we identify the need for AI-based weed management against herbicide resistance, comparative evaluation of chemical vs. non-chemical management, advances in remote sensing, and AI technology for weed identification, mapping, and management. We anticipate the ideas will contribute as a forum for establishing and adopting proven AI-based technologies in controlling more weed species across the world.

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Lack of precipitation and groundwater for irrigation limits crop production in semi-arid regions, such as the Southern High Plains (SHP). Advanced technologies, such as variable rate irrigation (VRI), can conserve water and improve water use efficiency for sustainable agriculture. However, the adoption of VRI is hindered by the lack of on-farm research focusing on the feasibility of VRI. The objective of this study was to assess the effect of irrigation rates on cotton yield as affected by soil physical properties and topography in the Southern High Plains. This study was conducted in two fields within a 194-ha commercially managed farm in Hale County, Texas, in 2017. An irrigation treatment with three rates was implemented in a randomized complete block design with two replications as separate blocks in each field. A total of 230 composite soil samples were collected from the farm in spring 2017 and analyzed for texture. Information on apparent soil electrical conductivity (ECa), elevation, and final yield were collected from the fields. A statistical model showed that the effect of irrigation rates on cotton yield depended on its interaction with soil physical properties and topography. For example, areas with slope >2% and sand content >50% had no significant response to higher irrigation rates. This model suggests that applying irrigation amounts based on the yield response can be a basis for VRI. This study provides valuable information for site-specific irrigation to optimize crop production in fields with significant variability in soil physical properties and topography.

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