Location: Sustainable Water Management Research
Title: Eddy Covariance quantification of soybean (Glycine Max L.) crop coefficients in a farmer’s field in a humid climateAuthor
Anapalli, Saseendran | |
KRUTZ, JASON - Mississippi State University | |
PINNAMANENI, SRINIVASA - Oak Ridge Institute For Science And Education (ORISE) | |
Reddy, Krishna | |
Fisher, Daniel |
Submitted to: Irrigation Science
Publication Type: Peer Reviewed Journal Publication Acceptance Date: 6/8/2021 Publication Date: 6/13/2021 Citation: Anapalli, S.S., Krutz, J., Pinnamaneni, S.R., Reddy, K.N., Fisher, D.K. 2021. Eddy Covariance quantification of soybean (Glycine Max L.,) water requirements and crop coefficients in a humid climate. Irrigation Science. 39:651–669. https://doi.org/10.1007/s00271-021-00742-2. DOI: https://doi.org/10.1007/s00271-021-00742-2 Interpretive Summary: Sustainability of irrigated agriculture in the MS Delta region is in jeopardy today due to water withdrawals for irrigation from the Mississippi River Valley Aquifer that exceed its natural recharge rates. This aquifer is nearly the sole source of groundwater for irrigating crops in the Mississippi Delta region. To reduce further loss of this aquifer as an irrigation water resource, crop-irrigation practices need to be based on accurate knowledge of water requirements of crops in response to realized weather variabilities in the field. To better understand this, scientists with the USDA ARS Sustainable Water Management Research Unit and Crop Production Systems Research Unit, Stoneville, MS, and Mississippi Water Resources Research Institute, Starkville, Mississippi, measured water requirements of soybean in a silt loam soil in a farmer’s field in the Lower Mississippi (MS) Delta, USA. For developing irrigation schedules across soils and climates other than the location in which they were measured, this study developed crop coefficients that link a crop water requirement calculated from local weather data to the measured crop water requirements. In this study, a cutting-edge science-based approach known as the ‘eddy covariance (EC) method’ was used for quantifying crop water requirements. This study is expected to help water resource managers sustain the groundwater resources in the MS valley alluvial aquifer using irritated-agriculture practices that promote water conservation. Technical Abstract: For sustainable irrigated agriculture, scheduling irrigations based on accurate estimates of crop water requirements (ETc, crop evapotranspiration) is critical. In weather-based irrigation scheduling, ETc is estimated as a product of a reference crop evapotranspiration (ET) computed from weather data and a crop coefficient (Kc). In this investigation, we used an eddy covariance (EC) method for quantifying soybean (cv. Asgro 46X4) ETc in silty clay soil in a humid climate. Quantified ETc was used for developing Kc for alfalfa (Kcr) and grass (Kco) reference crops computed from measured weather data. Experiments were conducted during three crop seasons (2017-19) in a 250-ha furrow-irrigated soybean field - planted in late April to early May and harvested in September. Water flux from the crops was estimated using EC instrumentation consisting of a 3D sonic anemometer and an open-path infrared gas analyzer installed in the constant flux layer above the crop canopy. Harvested soybean yields were 4771, 5783, and 4909 kg ha-1, consuming, 584, 640, and 593 mm ETc (average 605 mm), respectively, in 2017, 2018, and 2019. Monthly averaged daily ETc across the crop seasons varied between 2.1 mm in May 2019 to 6.2 mm in June 2018. Seasonally averaged daily ETc across the three-crop seasons varied between 4.3 and 5.2 mm with an average of 4.8 mm. Across the crop seasons, ETc was 22% less and 2% greater than computed grass (ETo) and alfalfa (ETr) reference crop ET. Monthly averaged daily Kco varied between 0.79 and 1.18, and Kcr ranged between 0.65 and 0.97. The Kc established can help in developing soybean irrigation schedules, across climates and soils, based on grass and soybean reference crop ET computed from realtime weather data. |