Here we present results from all three regional analyses for thresholds of 50km and 150km, alongside the main results. Tables 3‐4 present results for the Mekong River Basin, India, and global results, respectively. In each table, results from 9 regressions are shown. The specification across all regressions are the same, with the only variation coming from the distance to monitoring station threshold and the share of cropland threshold. By changing the distance threshold, the station that each gridcell is matched to does not change, as gridcells are always matched to their nearest upstream station. Rather, what is changed is the sample of gridcells that are included in the regression, with more gridcells included as the threshold increases. Note that in each table, columns 2, 5, and 8 are the regressions depicted in 4 for cropland thresholds of 30, 75, and 90 percent, respectively. In all regions, the coefficient on EC is not sensitive to changes in the monitoring station distance threshold. Similarly, weather control variables as not sensitive as well, implying that changes to the sample to not have a significant effect on the estimates.

We next test different thresholds of high EC tested for in the Mekong River Basin and India regions. In the main results, we use 100mS/m. The true sensitivity of agriculture to EC varies by crop. The original threshold was chosen as this is the recommendation made by the Mekong River Commission and other sources as to where one should start seeing impacts on irrigated crops (Kongmeng and Larsen 2014; Tanji and Kielen 2002). However, as shown in figure 5, impacts globally are found at lower values of EC. While the Mekong River Basin and India data sets do not offer enough variation to precisely estimate a semi‐ parametric model like that in figure 5, we do use alternative cut offs of 75 and 125 mS/m. Results are shown in Tables 6 and 7 for the Mekong River Basin and India, respectively. In both cases, we see that impacts are found at all thresholds. In both cases, results are monotonic and scale with the threshold that is used.

# 6. Simulating Global Impacts of Water Salinity on Agricultural Production

The results of this paper demonstrate just how sensitive global agricultural production is to salinity in water. Using this information, we perform a simulation to quantify the magnitude of agricultural losses that saline water causes. The GEMStat database, though having wide global coverage, does not cover all land where there is agricultural production. Notably, big gaps in the data are present in China and Sub‐ Saharan Africa, two major agricultural areas where high salinity might be a problem (Figure 3).

To fill gaps in the GEMS data, we train a machine learning model to predict continuous values of EC globally at a 0.5‐degree scale between 1992 and 2013. More specifically, we construct a data set covering the drivers of salinity identified in the scientific literature and use a Random Forest algorithm which finds the best fit between observed and predicted values over other commonly used algorithms (linear regressions, support vector machines, etc.). To estimate the model, we proceed as follows. First, we combine EC data from India, the Mekong Basin and GEMStat to have the broadest geographical coverage as possible. Second, we randomly split our data set between a training sample (80% of the observations) and a testing sample (20% of the observations). Third, we train the model on the testing sample and assess the predictive power on the testing sample. Fourth, we choose the best model as the one that maximizes the correlation coefficient between observed and predicted values in the testing sample. Finally, we use the best model to predict EC for every year between 1992 and 2013 globally at a 0.5 degree scale. More information on the covariates used and the estimation procedure can be found in Desbureaux et al (2019), which extends the approach to more parameters. The correlation coefficient between observed and predicted values of EC in the testing sample is 88 percent. Figure 6 shows the average predicted EC for each gridcell from 1992‐2013. Where values are poorly predicted, the predicted EC tends to be lower than 8