Influence of Irrigation Drivers Using Boosted Regression Trees: Kansas High Plains

Groundwater levels across parts of western Kansas have been declining at unsustainable rates due to pumping for agricultural irrigation despite water-saving efforts. Accelerating this decline is the complex agricultural landscape, consisting of both categorical (e.g., management boundaries) and numerical (e.g., crop prices) factors that drive irrigation decisions, making integrated water budget management a challenge. Furthermore, these factors frequently change through time, rendering management strategies outdated within relatively short time scales. This study uses boosted regression trees to simultaneously analyze categorical and numerical data against annual irrigation pumping to determine the relative influence of each factor on groundwater pumping across both space and time. In all, 45 key water use variables covering approximately 19,000 groundwater wells were tested against irrigation pumping from 2006 to 2016 across five categories: (1) management/policy, (2) hydrology, (3) weather, (4) land/agriculture, and (5) economics. Study results showed that variables from all five categories were included among the top 10 drivers to irrigation, and the greatest influence came from variables such as irrigated area per well, saturated thickness, soil permeability, summer precipitation, and pumping costs (depth to water table). Variables that had little influence included regional management boundaries and irrigation technology. The results of this study are further used to target the factors that statistically lead to the greatest volumes of groundwater pumping to help develop robust management strategy suggestions and achieve water management goals of the region. Plain Language Summary Water use for crops has lowered groundwater levels in western Kansas. Past studies have shown that this water use is driven by many factors spanning policy, economics, and the physical environment. Because of this complexity, it has been difficult to fully understand which factors most drive irrigation use relative to each other. This study uses a machine-learning model to rank the influence of 45 factors on irrigation pumping. These factors are analyzed over space (∼19,000 wells across western Kansas) and time (2006–2016). Based on this study, drivers to water use include total irrigated area, summer rainfall, and depth to the water table. Factors that have little influence include management district boundaries and irrigation system type. These results are used to make water management suggestions for the region

Impacts of residential fertilizer ordinances on Florida lacustrine water quality

Abstract Despite the assumption that residential fertilizer ordinances improve regional water quality, their impacts across space and time largely remain unknown. Here, we analyze changes in water quality of lakes throughout the State of Florida from 1987 to 2018, comparing trends in water quality parameters before and after implementation of county‐wide fertilizer ordinances. We used a large dataset of publicly collected water quality data and linear mixed models to analyze ordinance impacts on total nitrogen, total phosphorus, chlorophyll a, and Secchi depth across 160 lakes throughout Florida. We further analyze water quality impacts relative to the type of ordinance (winter fertilizer ban, summer ban, nonseasonal ban, no ban). We found fertilizer ordinances favorably impacted lacustrine water quality, and winter (dry season) fertilizer bans had the greatest effect across all water quality metrics. Results of this study can be used to support the effectiveness of fertilizer ordinances across humid tropical and subtropical climate regions

A diet high in resistant starch modulates microbiota composition, SCFA concentrations, and gene expression in pig intestine

Resistant starch (RS) is highly fermentable by microbiota in the colon, resulting in the production of SCFAs. RS is thought to mediate a large proportion of its health benefits, including increased satiety, through the actions of SCFAs. The aim of this study was to investigate the effects of a diet high in RS on luminal microbiota composition, luminal SCFA concentrations, and the expression of host genes involved in SCFA uptake, SCFA signaling, and satiety regulation in mucosal tissue obtained from small intestine, cecum, and colon. Twenty adult female pigs were either assigned to a digestible starch (DS) diet or a diet high in RS (34%) for a period of 2 wk. After the intervention, luminal content and mucosal scrapings were obtained for detailed molecular analysis. RS was completely degraded in the cecum. In both the cecum and colon, differences in microbiota composition were observed between DS- and RS-fed pigs. In the colon these included the stimulation of the healthy gut-associated butyrate-producing Faecalibacterium prausnitzii, whereas potentially pathogenic members of the Gammaproteobacteria, including Escherichia coli and Pseudomonas spp., were reduced in relative abundance. Cecal and colonic SCFA concentrations were significantly greater in RS-fed pigs, and cecal gene expression of monocarboxylate transporter 1 (SLC16A1) and glucagon (GCG) was induced by RS. In conclusion, our data show that RS modulates microbiota composition, SCFA concentrations, and host gene expression in pig intestine. Combined, our data provide an enhanced understanding of the interaction between diet, microbiota, and host