Saseendran Anapalli

Dr. Saseendran Anapalli

Sustainable Water Management Research Unit, USDA-Agricultural Research Service, Ft Collins, CO 80526 USA
orcid

Research Focus & Metrics

Academic Career Length

11Years (2016-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
6 July 2020 in Global Change Biology
Verified
Journal Article
Closed access
PDF file
GFGatien N. Falconnier
Gatien N. Falconnier
MCMarc Corbeels
Marc Corbeels
KBKenneth J. Boote
Kenneth J. Boote
FAFrançois Affholder
François Affholder
MAMyriam ADAM
Myriam ADAM
HWHeidi Webber
Heidi Webber

1 November 2016 in Agricultural Water Management
Verified
Journal Article
Closed access
Saseendran AnapalliSaseendran Anapalli
Saseendran Anapalli
LALajpat R. Ahuja
Lajpat R. Ahuja
PGPrasanna H. Gowda
Prasanna H. Gowda
LMLiwang Ma
Liwang Ma
GMGary Marek
Gary Marek
THTerry A. Howell
Terry A. Howell

1 May 2019 in Science of The Total Environment
Verified
Journal Article
Closed access
Saseendran AnapalliSaseendran Anapalli
Saseendran Anapalli
DFDaniel K. Fisher
Daniel K. Fisher
KRKrishna N. Reddy
Krishna N. Reddy
JKJason L. Krutz
Jason L. Krutz
SPSrinivasa R. Pinnamaneni
Srinivasa R. Pinnamaneni
RSRuixiu Sui
Ruixiu Sui

28 October 2016 in Climate
Verified
Journal Article
Open access
PDF file
Saseendran AnapalliSaseendran Anapalli
Saseendran Anapalli
DFDaniel Fisher
Daniel Fisher
KRKrishna N. Reddy
Krishna N. Reddy
WPWilliam T. Pettigrew
William T. Pettigrew
RSRuixiu Sui
Ruixiu Sui
LALajpat R. Ahuja
Lajpat R. Ahuja
Anthropogenic activities continue to emit potential greenhouse gases (GHG) into the atmosphere leading to a warmer climate over the earth. Predicting the impacts of climate change (CC) on food and fiber production systems in the future is essential for devising adaptations to sustain production and environmental quality. We used the CSM-CROPGRO-cotton v4.6 module within the RZWQM2 model for predicting the possible impacts of CC on cotton (Gossypium hirsutum) production systems in the lower Mississippi Delta (MS Delta) region of the USA. The CC scenarios were based on an ensemble of climate projections of multiple GCMs (Global Climate Models/General Circulation Models) for climate change under the CMIP5 (Climate Model Inter-comparison and Improvement Program 5) program, that were bias-corrected and spatially downscaled (BCSD) at Stoneville location in the MS Delta for the years 2050 and 2080. Four Representative Concentration Pathways (RCP) drove these CC projections: 2.6, 4.5, 6.0, and 8.5 (these numbers refer to radiative forcing levels in the atmosphere of 2.6, 4.5, 6.0, and 8.5 W·m−2), representing the increasing levels of the greenhouse gas (GHG) emission scenarios for the future, as used in the Intergovernmental Panel on Climate Change-Fifth Assessment Report (IPCC-AR5). The cotton model within RZWQM2, calibrated and validated for simulating cotton production at Stoneville, was used for simulating production under these CC scenarios. Under irrigated conditions, cotton yields increased significantly under the CC scenarios driven by the low to moderate emission levels of RCP 2.6, 4.5, and 6.0 in years 2050 and 2080, but under the highest emission scenario of RCP 8.5, the cotton yield increased in 2050 but declined significantly in year 2080. Under rainfed conditions, the yield declined in both 2050 and 2080 under all four RCP scenarios; however, the yield still increased when enough rainfall was received to meet the water requirements of the crop (in about 25% of the cases). As an adaptation measure, planting cotton six weeks earlier than the normal (historical average) planting date, in general, was found to boost irrigated cotton yields and compensate for the lost yields in all the CC scenarios. This early planting strategy only partially compensated for the rainfed cotton yield losses under all the CC scenarios, however, supplemental irrigations up to 10 cm compensated for all the yield losses.

1 May 2018 in Agricultural Water Management
Verified
Journal Article
Closed access
Saseendran AnapalliSaseendran Anapalli
Saseendran Anapalli
Timothy GreenTimothy Green
Timothy Green
KRKrishna N. Reddy
Krishna N. Reddy
PGPrasanna H. Gowda
Prasanna H. Gowda
RSRuixiu Sui
Ruixiu Sui
GMGary W. Marek
Gary W. Marek

29 September 2016 in Agronomy
Verified
Journal Article
Open access
PDF file
Saseendran AnapalliSaseendran Anapalli
Saseendran Anapalli
WPWilliam T. Pettigrew
William T. Pettigrew
KRKrishna N. Reddy
Krishna N. Reddy
LMLiwang Ma
Liwang Ma
DFDaniel Fisher
Daniel Fisher
RSRuixiu Sui
Ruixiu Sui
Unique, variable summer climate of the lower Mississippi (MS) Delta region poses a critical challenge to cotton producers in deciding when to plant for optimized production. Traditional 2–4 year agronomic field trials conducted in this area fail to capture the effects of long-term climate variabilities in the location for developing reliable planting windows for producers. Our objective was to integrate a four-year planting-date field experiment conducted at Stoneville, MS during 2005–2008 with long-term climate data in an agricultural system model and develop optimum planting windows for cotton under both irrigated and rainfed conditions. Weather data collected at this location from 1960 to 2015 and the CSM-CROPGRO-Cotton v4.6 model within the Root Zone Water Quality Model (RZWQM2) were used. The cotton model was able to simulate both the variable planting date and variable water regimes reasonably well: relative errors of seed cotton yield, aboveground biomass, and leaf area index (LAI) were 14%, 12%, and 21% under rainfed conditions and 8%, 16%, and 15% under irrigated conditions, respectively. Planting windows under both rainfed and irrigated conditions extended from mid-March to mid-June: windows from mid-March to the last week of May under rainfed conditions, and from the last week of April to the end of May under irrigated conditions were better suited for optimum yield returns. Within these windows, rainfed cotton tends to lose yield from later plantings, but irrigated cotton benefits; however, irrigation requirements increase as the planting windows advance in time. Irrigated cotton produced about 1000 kg·ha−1 seed cotton more than rainfed cotton, with irrigation water requirements averaging 15 cm per season. Under rainfed conditions, there is a 5%, 14%, and 27% chance that the seed cotton production is below 1000, 1500, and 2000 kg·ha−1, respectively. Information developed in this paper can help MS farmers in decision support for cotton planting.