Supporting Material for: "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network" 1: Computing and software requirements These scripts and associated fortran programs were run in a OSX (10.14.5) C-Shell command line environment. The plotting scripts require Generic Mapping Tools (GMT: https://www.soest.hawaii.edu/gmt/) The executables for the fortran source code is included. If they don't work on your Unix-like platform you will need to compile them. Because the simulated yield data is stored in two netcdf files you will also have to install and link with the netcdf software library. A makefile is included but would only work if you are running Absoft Fortran ver 2018.0 and netcdf 4. Even then (in my experience) its unlikely that everything would compile and work as is. Otherwise, the makefile can used as a template for whatever Unix platform you are using. If you just want to see the output of the fortran programs, it is contained in the various log files that are included, i.e., Fig3abcd.log Fig3efgh.log Fig4a.log Fig4b.log Fig5a.log Fig5b.log Fig6ab.log Fig6cd.log Fig7.log calc_CvsS_sdrf.11.25.0.000.0.0003.log (For Fig. 8a) calc_CvsS_sdrf.32.25.0.000.0.0003.log (For Fig. 8b) mo_names.out The weather data necessary to plot two of the the paper's figures (Fig. 5c) can't be re-distributed per an agreement with Texas Tech University. Each script plots via the OSX Preview utility. CONTENTS 2: program_flow.ppt Powerpoint file. Illustrates the association between scripts,input files, fortran programs, output files and GMT utilities. 3: Plotting Scripts PLTFIG2MAP.scr PLTFIG3abcd.scr PLTFIG3efgh.scr PLTFIG4a.scr PLTFIG4b.scr PLTFIG5a.scr PLTFIG5b.scr PLTFIG6ab.scr PLTFIG6cd.scr PLTFIG7.scr PLTFIG8a.scr PLTFIG8b.scr CALC_CvsS_SDRF.scr CALC_COT_KWALLIS.scr CALC_SOR_KWALLIS.scr 4: Simulated Rainfed Cotton and Sorghum yields in netcdf files. SCYIELD.PDATE.NC (DSSAT CROPGRO-Cotton simulations 21 Stations X 12 Years) SRYIELD.PDATE2.NC (DSSAT CERES-Sorghum simulations 21 Stations X 12 Years) 5: Other data files. cCosts.dat -- Dryland cotton production costs sCosts.dat -- Dryland sorghum production costs NASS_Prices_2000-2019.log -- NASS Texas monthly cotton and sorghum prices nasslint_dists.txt -- NASS Dist 11 & 12 dryland cotton yield survey percentiles nass_sgx_dists.txt -- NASS Dist 11 & 12 dryland sorghum yield survey percentiles 6: Fortran source code. calc_CvsS_ploss.f calc_CvsS_sdrf.f calc_allcotyld.f calc_allsoryld.f calc_cot_pft.f calc_sor_pft.f plt_sdrf.f dhbarf4.f Generates GMT input files for drawing horizontal bar plots dvbarf4.f Generates GMT input files for drawing vertical bar plots

The main data sets used here are simulated rainfed cotton and sorghum yields generated via the DSSAT CROPGRO-Cotton and CERES-Sorghum crop models. The model's weather inputs were provided by 21 Texas Tech University mesonet stations over an 11-year period. This weather data can't be re-distributed per an agreement with Texas Tech University. Given the 231 station-years of weather input data, the models are used to generate similarly dense yield distributions. Model simulations for both crops are repeated over 32 planting dates to find those that maximize median lint and grain yields. After yield scaling to adjust the aggregate median of simulated yields over all planting dates to agree with median reported Southern High Plains (SHP) rainfed cotton and sorghum yields, the resulting yield distributions are converted to profit distributions. These distributions are formed based on fixed production costs, but variable lint and grain commodity values representative of market conditions since 2005. The resulting simulation chain thus transforms dense samples of growing season weather variability into similarly dense distributions of yield and profit outcomes that are consistent with current SHP summer growing conditions and recent market conditions. The yield and profit distributions produced by this chain can be used to determine optimal planting dates of both crops, estimate the profit and risk effects of management, andcompare the profitability of rainfed cotton and sorghum over a range of commodity prices.

Without reliable seasonal climate forecasts, farmers and managers in other weather-sensitive sectors might adopt practices that are optimal for recent climate conditions. To demonstrate this principle, crop simulation models driven by a dense meteorological network were used to identify climate-optimal planting dates for U.S. Southern High Plains (SHP) un-irrigated agriculture. This method converted large samples of SHP growing season weather outcomes into climate-representative cotton and sorghum yield distributions over a range of planting dates. Best planting dates were defined as those that maximized median cotton lint (April 24) and sorghum grain (July 1) yields. Those optimal yield distributions were then converted into corresponding profit distributions reflecting 2005-2019 commodity prices and fixed production costs. Both crop's profitability under variable price conditions and current SHP climate conditions were then compared based on median profits and loss probability, and through stochastic dominance analyses that assumed a slightly risk-averse producer.