Dataset Information

Assessing the impacts of irrigation termination periods on cotton productivity under strategic deficit irrigation regimes.

ABSTRACT: Determining optimum irrigation termination periods for cotton (Gossypium hirsutum L.) is crucial for efficient utilization and conservation of finite groundwater resources of the Ogallala Aquifer in the Texas High Plains (THP) region. The goal of this study was to suggest optimum irrigation termination periods for different Evapotranspiration (ET) replacement-based irrigation strategies to optimize cotton yield and irrigation water use efficiency (IWUE) using the CROPGRO-Cotton model. We re-evaluated a previously evaluated CROPGRO-Cotton model using updated yield and in-season physiological data from 2017 to 2019 growing seasons from an IWUE experiment at Halfway, TX. The re-evaluated model was then used to study the effects of combinations of irrigation termination periods (between August 15 and September 30) and deficit/excess irrigation strategies (55%-115% ET-replacement) under dry, normal and wet years using weather data from 1978 to 2019. The 85% ET-replacement strategy was found ideal for optimizing irrigation water use and cotton yield, and the optimum irrigation termination period for this strategy was found to be the first week of September during dry and normal years, and the last week of August during wet years. Irrigation termination periods suggested in this study are useful for optimizing cotton production and IWUE under different levels of irrigation water availability.

SUBMITTER: Himanshu SK

PROVIDER: S-EPMC8505508 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Precision irrigation technologies using sensor feedback can provide dynamic decision support to help farmers implement DI strategies. However, few studies have reported on the use of these systems for DI management. This two-year study was conducted in Bushland, Texas to investigate the performance of the geographic information (GIS) based irrigation scheduling supervisory control and data acquisition (ISSCADA) system as a tool to manage deficit irrigation scheduling for cotton (Gossypim hirsutum L). Two different irrigation scheduling methods automated by the ISSCADA system - (1) a plant feedback (designated C) - based on integrated crop water stress index (iCWSI) thresholds, and (2) a hybrid (designated H) method, created to combine soil water depletion and the iCWSI thresholds, were compared with a benchmark manual irrigation scheduling (M) that used weekly neutron probe readings. Each method applied irrigation at levels designed to be equivalent to 25%, 50% and 75% replenishment of soil water depletion to near field capacity (designated I25, I50 and I75) using the pre-established thresholds stored in the ISSCADA system or the designated percent replenishment of soil water depletion to field capacity in the M method. Fully irrigated and extremely deficit irrigated plots were also established. Relative to the fully irrigated plots, deficit irrigated plots at the I75 level for all irrigation scheduling methods-maintained seed cotton yield, while saving water. In 2021, the irrigation savings was a minimum of 20%, while in 2022, the minimum savings was 16%. Comparing the performance of deficit irrigation scheduling between the ISSCADA system and the manual method showed that crop response for all three methods were statistically similar at each irrigation level. Because the M method requires labor intensive and expensive use of the highly regulated neutron probe, the automated decision support provided by the ISSCADA system could simplify deficit irrigation management of cotton in a semi-arid region.

Project description:The productivity of permanent temperate cut grasslands is mainly driven by weather, soil characteristics, botanical composition and management. To adapt management to climate change, adjusting the cutting dates to reflect earlier onset of growth and expansion of the vegetation period is particularly important. Simulations of cut grassland productivity under climate change scenarios demands management settings to be dynamically derived from actual plant development rather than using static values derived from current management operations. This is even more important in the alpine region, where the predicted temperature increase is twice as high as compared to the global or Northern Hemispheric average. For this purpose, we developed a dynamic management module that provides timing of cutting and manuring events when running the biogeochemical model LandscapeDNDC. We derived the dynamic management rules from long-term harvest measurements and monitoring data collected at pre-alpine grassland sites located in S-Germany and belonging to the TERENO monitoring network. We applied the management module for simulations of two grassland sites covering the period 2011-2100 and driven by scenarios that reflect the two representative concentration pathways (RCP) 4.5 and 8.5 and evaluated yield developments of different management regimes. The management module was able to represent timing of current management operations in high agreement with several years of field observations (r² > 0.88). Even more, the shift of the first cutting dates scaled to a +1 °C temperature increase simulated with the climate change scenarios (-9.1 to -17.1 days) compared well to the shift recorded by the German Weather Service (DWD) in the study area from 1991-2016 (-9.4 to -14.0 days). In total, the shift in cutting dates and expansion of the growing season resulted in 1-2 additional cuts per year until 2100. Thereby, climate change increased yields of up to 6 % and 15 % in the RCP 4.5 and 8.5 scenarios with highest increases mainly found for dynamically adapted grassland management going along with increasing fertilization rates. In contrast, no or only minor yield increases were associated with simulations restricted to fertilization rates of 170 kg N ha-1 yr-1 as required by national legislations. Our study also shows that yields significantly decreased in drought years, when soil moisture is limiting plant growth but due to comparable high precipitation and water holding capacity of soils, this was observed mainly in the RCP 8.5 scenario in the last decades of the century.

Dataset Information

Assessing the impacts of irrigation termination periods on cotton productivity under strategic deficit irrigation regimes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets