Recently, cyanobacteria blooms have become a concern for agricultural irrigation water quality. Numerous studies have shown that cyanotoxins from these harmful algal blooms (HABs) can be transported to and assimilated into crops when present in irrigation waters. Phycocyanin is a pigment known only to occur in cyanobacteria and is often used to indicate cyanobacteria presence in waters. The objective of this work was to identify the most influential environmental covariates affecting the phycocyanin concentrations in agricultural irrigation ponds that experience cyanobacteria blooms of the potentially toxigenic species Microcystis and Aphanizomenon using machine learning methodology. The study was performed at two agricultural irrigation ponds over a 5-month period in the summer of 2018. Phycocyanin concentrations, along with sensor-based and fluorometer-based water quality parameters including turbidity (NTU), pH, dissolved oxygen (DO), fluorescent dissolved organic matter (fDOM), conductivity, chlorophyll, color dissolved organic matter (CDOM), and extracted chlorophyll were measured. Regression tree analyses were used to determine the most influential water quality parameters on phycocyanin concentrations. Nearshore sampling locations had higher phycocyanin concentrations than interior sampling locations and “zones” of consistently higher concentrations of phycocyanin were found in both ponds. The regression tree analyses indicated extracted chlorophyll, CDOM, and NTU were the three most influential parameters on phycocyanin concentrations. This study indicates that sensor-based and fluorometer-based water quality parameters could be useful to identify spatial patterns of phycocyanin concentrations and therefore, cyanobacteria blooms, in agricultural irrigation ponds and potentially other water bodies.

近来，蓝藻水华已成为农业灌溉水质量的关注点。大量研究表明，这些有害藻华（HAB）产生的氰毒素在灌溉水中存在时，可以运输到农作物并吸收到农作物中。藻蓝蛋白是仅在蓝细菌中存在的色素，通常用于指示水中存在蓝细菌。这项工作的目的是确定最有影响力的环境协变量，这些变量会影响农业灌溉池塘中藻蓝蛋白的浓度，这些池塘会经历潜在的产毒物种微囊藻和Aphanizomenon的蓝细菌绽放。使用机器学习方法。该研究在2018年夏季的两个月内在两个农业灌溉池塘中进行。藻蓝蛋白浓度以及基于传感器和基于荧光计的水质参数，包括浊度（NTU），pH，溶解氧（DO），荧光溶解的有机物质（˚F测量了DOM，电导率，叶绿素，颜色溶解的有机物（CDOM）和提取的叶绿素。回归树分析用于确定对藻蓝蛋白浓度影响最大的水质参数。近岸采样点的藻蓝蛋白浓度高于内部采样点，两个池塘中均发现了藻蓝蛋白浓度始终较高的“区域”。回归树分析表明，提取的叶绿素，CDOM和NTU是对藻蓝蛋白浓度影响最大的三个参数。这项研究表明，基于传感器和基于荧光计的水质参数可能有助于确定藻蓝蛋白浓度的空间格局，从而确定农业灌溉池塘和其他潜在水体中的蓝藻水华。