Performance of Precision Mobile Drip Irrigation in the Texas High Plains Region

O’Shaughnessy, Susan A.; Colaizzi, Paul D.

doi:10.3390/agronomy7040068

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…The 63 rd , and 50 th DAPs however, belong to vegetative growth stage. Here, the important variables selected by machine learning algorithm were all before blister stage, shortly after pollination when the maize kernels are just forming [ 60 ]. It is unknown if the importance of this late vegetative to early reproductive period was unique to this studies germplasm and environments, or if it has greater general applicability.…”

Section: Discussionmentioning

confidence: 99%

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Chatterjee

Adak²,

Wilde³

et al. 2023

PLoS ONE

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Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68–72%), but inconsistent models. A little sacrifice in accuracy (~60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

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Section: Discussionmentioning

confidence: 99%

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Chatterjee

Adak²,

Wilde³

et al. 2023

PLoS ONE

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“…A 2015 corn study with irrigation capacity treatments limited to 3.1 mm d -1 resulted in MDI having 35% lower soil water evaporation and significantly greater end-of season soil water compared to LESA (Kisekka et al, 2017). In a similar 2015-2016 corn study, MDI resulted in greater WUE than either LESA or LEPA in the dryer 2016 growing season (O'Shaughnessy and Colaizzi, 2017). Comparisons of LEPA to MDI at Halfway, Texas, showed no consistent differences in yield between the two methods for grain sorghum or cotton (table 4).…”

Section: Other Irrigation Researchmentioning

confidence: 99%

Low-Energy Precision Application (LEPA) Irrigation: A Forty-Year Review

Bordovsky¹

2019

Transactions of the ASABE

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Abstract. The low-energy precision application (LEPA) irrigation concept was developed 40 years ago (ca. 1978) to address the depletion of irrigation water from the Ogallala Aquifer and the sharp increase in pumping costs caused by the 1970s fuel crisis occurring at that time in the Texas High Plains. The LEPA method applies water to the soil surface at low pressure using a tower-truss irrigation system that continually moves through the field. This method brought changes in irrigation equipment and management that resulted in improvements in water productivity, particularly in semi-arid locations with diminishing water supplies. A review of published information pertaining to LEPA history, evaluation, and usage was performed. On landscapes of less than 1% slope, negative crop yield effects caused by irrigation runoff and start-stop system alignment were overcome with appropriately spaced basins, or furrow checks, and multiple irrigations over the course of the growing season. No consistent yield advantage at any level of irrigation was documented by placing water in every furrow (1 m spacing) compared to alternate furrows (2 m spacing). In irrigation treatments having =50% of the estimated full irrigation quantity, LEPA resulted in a 16% yield increase over sprinkler methods, although subsurface drip irrigation (SDI) resulted in a 14% yield increase over LEPA. At irrigation levels >50% of full irrigation, crop yields of sprinkler treatments were only slightly less than those of LEPA, and SDI yields were 7% greater than LEPA. The LEPA irrigation method was the catalyst for innovations in chemigation, no-till planting, and site-specific irrigation. As irrigation water becomes more limited, use and proper management of optimum irrigation methods will be critical. Keywords: Basin tillage, Chemigation, Evapotranspiration, Irrigation methods, LEPA, Low-energy precision application, Runoff, Spray irrigation, Sprinkler irrigation, Uniformity, Water use efficiency.

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“…An MDI system was recently commercialized as a retrofit to existing center pivot and lateral move irrigation systems. The MDI system applies water through specially designed surface driplines that are pulled through fields by the irrigation machine (Kisekka et al, 2017;O'Shaughnessy and Colaizzi, 2017). Similar to the LEPA method, which applies water on the soil surface between crop rows, MDI reduces water losses due to spray evaporation, evaporation from a wetted canopy, and wind drift resulting in increased efficiency.…”

Section: Gravitymentioning

confidence: 99%

“…One advantage MDI may have over LEPA or LESA is its potential use on topography of greater slope without using furrow dikes, due to its extended wetting pattern. Studies in Texas and Kansas have shown that MDI results in improved CWP compared with LEPA or LESA for corn in years with average to below average precipitation, but not for years with above average precipitation (Kisekka et al, 2017;O'Shaughnessy and Colaizzi, 2017). Additional studies are clearly needed to test MDI vs. LEPA or LESA for cotton, particularly in drought conditions.…”

Section: Gravitymentioning

confidence: 99%

Forty Years of Increasing Cotton’s Water Productivity and Why the Trend Will Continue

Barnes¹,

Campbell²,

Vellidis³

et al. 2020

Applied Engineering in Agriculture

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Highlights Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased leading to a large increase in crop water productivity (CWP). Many factors have contributed to improved CWP, such as improvements in water delivery systems. Irrigation scheduling technologies have also contributed to improved CWP; however, farmer adoption of advanced scheduling technologies is still limited and there is significant room for improvement. Increased yields from improved cultivars without an increase in water requirements has also been important for CWP. Continued developments in sensor technologies and improved crop simulation models are two examples of future strategies that should allow the U.S. cotton industry to continue an upward trend in CWP. Abstract. Over the last 40 years the amount of irrigation water used by cotton in the United States has decreased while yields have increased. Factors contributing to higher water productivity and decreased irrigation water use include migration of cotton out of the far western U.S. states to the east where more water requirements are met by rainfall; improved irrigation delivery systems with considerable variation in types and adoption rates across the U.S.; improved irrigation scheduling tools; improved genetics and knowledge of cotton physiology, and improved crop models that can help evaluate new irrigation strategies rapidly and inexpensively. The considerable progress over the last 40 years along with the promise of emerging technologies suggest that this progress will continue. Keywords: Cotton, Crop water productivity, Irrigation, Sustainability, Water use efficiency.

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Performance of Precision Mobile Drip Irrigation in the Texas High Plains Region

Cited by 11 publications

References 18 publications

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Cumulative temporal vegetation indices from unoccupied aerial systems allow maize (Zea mays L.) hybrid yield to be estimated across environments with fewer flights

Low-Energy Precision Application (LEPA) Irrigation: A Forty-Year Review

Forty Years of Increasing Cotton’s Water Productivity and Why the Trend Will Continue

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