DOI: 10.5061/dryad.37pvmcvfx
title: Supporting Data and Code for "Managing to Climatology: Improving semi-arid agricultural risk management using crop models and a dense meteorological network"

description: 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.
description: 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. 
description: Supporting Material for: "Managing to Climatology: Improving
 semi-arid agricultural risk management using crop models and a dense
 meteorological network"<br> <br> 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<br>     will
 need to compile them. Because the simulated yield data is stored in two
 netcdf files you will also have to<br>     install and link with the
 netcdf software library. A makefile is included but would only work if you
 are running<br>     Absoft Fortran ver 2018.0 and netcdf 4. Even
 then (in my experience) its unlikely that everything would
 compile<br>     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 <br>     that are included,
 i.e.,      Fig3abcd.log<br>    
 Fig3efgh.log<br>     Fig4a.log<br>     Fig4b.log<br>    
 Fig5a.log<br>     Fig5b.log<br>     Fig6ab.log<br>    
 Fig6cd.log<br>     Fig7.log<br>    
 calc_CvsS_sdrf.11.25.0.000.0.0003.log  (For Fig. 8a)<br>    
 calc_CvsS_sdrf.32.25.0.000.0.0003.log  (For Fig. 8b)<br>    
 mo_names.out     The weather data necessary to plot two
 of the the paper's figures (Fig. 5c) can't be re-distributed
 per<br>     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<br>    PLTFIG3abcd.scr<br>  
  PLTFIG3efgh.scr<br>    PLTFIG4a.scr<br>  
  PLTFIG4b.scr<br>    PLTFIG5a.scr<br>  
  PLTFIG5b.scr<br>    PLTFIG6ab.scr<br>  
  PLTFIG6cd.scr<br>    PLTFIG7.scr<br>  
  PLTFIG8a.scr<br>    PLTFIG8b.scr<br>  
  CALC_CvsS_SDRF.scr<br>    CALC_COT_KWALLIS.scr<br>  
  CALC_SOR_KWALLIS.scr 4: Simulated Rainfed Cotton and
 Sorghum yields in netcdf files.<br>    <br>  
  SCYIELD.PDATE.NC (DSSAT CROPGRO-Cotton simulations 21 Stations X 12
 Years)<br>    SRYIELD.PDATE2.NC (DSSAT CERES-Sorghum simulations 21
 Stations X 12 Years) 5: Other data files.
    cCosts.dat  -- Dryland cotton production costs<br>  
  sCosts.dat  -- Dryland sorghum production costs<br>  
  NASS_Prices_2000-2019.log -- NASS Texas monthly cotton and sorghum
 prices<br>    nasslint_dists.txt -- NASS Dist 11 & 12
 dryland cotton  yield survey percentiles<br>    nass_sgx_dists.txt
 -- NASS Dist 11 & 12 dryland sorghum yield survey
 percentiles 6: Fortran source code.
    calc_CvsS_ploss.f<br>    calc_CvsS_sdrf.f<br>  
  calc_allcotyld.f<br>    calc_allsoryld.f<br>  
  calc_cot_pft.f<br>    calc_sor_pft.f<br>  
  plt_sdrf.f<br>    dhbarf4.f  Generates GMT input files for drawing
 horizontal bar plots<br>    dvbarf4.f  Generates GMT input files for
 drawing vertical bar plots