10.5061/DRYAD.37PVMCVFX
Mauget, Steven
0000-0003-1056-8495
Agricultural Research Service
Mitchell-McCallister, Donna
Texas Tech University
Supporting Data and Code for "Managing to Climatology: Improving
semi-arid agricultural risk management using crop models and a dense
meteorological network"
Dryad
dataset
2020
2021-06-25T00:00:00Z
2021-06-25T00:00:00Z
en
https://doi.org/10.5281/zenodo.4929947
228895 bytes
9
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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.
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