Canopy temperature, yield, and harvest index of corn as affected by planting geometry in a semi-arid environment

Thapa, Sushil; Stewart, Bob; Xue, Qingwu; Rhoades, Marty B.; Angira, Brijesh; Reznik, John W.

doi:10.1016/j.fcr.2018.08.009

Cited by 18 publications

(19 citation statements)

References 35 publications

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“…We did not observe a pattern among genetic background and management adaptation demonstrating that modern cultivars have not been optimized to certain management systems. This finding of differential SY performance dependent upon the agro-management system is consistent with other crop species 66,67 suggesting a need for prescriptive breeding solutions. According to a USDA survey, soybean producers use three row spacing’s treatments for more than 90% of planted acreage in the US making such prescriptive breeding methodologies important to breeding programs to produce more competitive cultivars for different management systems.…”

Section: Discussionsupporting

confidence: 82%

Machine Learning Approach for Prescriptive Plant Breeding

Parmley

Higgins

Ganapathysubramanian

et al. 2019

Sci Rep

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We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding.

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Section: Discussionsupporting

confidence: 82%

Machine Learning Approach for Prescriptive Plant Breeding

Parmley

Higgins

Ganapathysubramanian

et al. 2019

Sci Rep

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“…Water stress significantly decreased these variables, resulting in poor grain yield which was most evident in 2011. Previous studies in the THP also reported that water stress reduced plant growth and grain yield in corn (Colaizzi, Evett, & Howell, 2011; Howell et al., 1995; Thapa et al., 2018). Biomass increase during grain filling (silking to maturity) significantly contributed to grain yield in 2010 and in 2012.…”

Section: Discussionmentioning

confidence: 82%

“…The seasonal average irrigation for grain corn is approximately 500 mm in 26 counties of THP (Marek et al., 2011). However, the Ogallala Aquifer, the primary source of groundwater in the area, is declining in response to low recharge rate relative to withdrawals, creating uncertainty for future corn yields (Colaizzi, Gowda, Marek, & Porter, 2009; Roberts, Male, & Toombs, 2007; Thapa et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Corn production under restricted irrigation in the Texas High Plains

et al. 2020

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Sustainable corn (Zea mays L.) production in the semiarid Texas High Plains (THP) relies on irrigation water from the Ogallala Aquifer, but it is well known that the aquifer is declining. A 3-yr (2010-2012) field study was conducted to find whether successful corn production is possible using only 60% (300 mm) of the current average irrigation for the region (500 mm). Three corn hybrids (Pioneer P31G96, Pioneer P33D49, and Monsanto DKC67-87) were planted at four densities (5, 6, 7, and 8 plants m −2 ) under a center pivot sprinkler system. Seasonal precipitation was highest in 2010 (224 mm), followed by 2012 (132 mm), and 2011 (85 mm). Seasonal evapotranspiration for corn ranged between 426 and 635 mm. Yield was significantly lower in 2011 (2.4 Mg ha −1 ) and 2012 (6.0 Mg ha −1 ) than 2010 (13.3 Mg ha -1 ) due to severe drought conditions combined with unusually high temperatures in those years. Increase in planting density increased biomass at silking but did not increase yield. Grain yield was related to biomass at maturity, harvest index, and biomass increase during grain filling. Results suggest that corn yields of about 13 Mg ha −1 are possible with only 300 mm of irrigation in the THP, but production risk is high due to frequent years with low seasonal precipitation.Abbreviations: ET, evapotranspiration; HI, harvest index; PASW, plant available soil water; THP, Texas High Plains; TKW, thousand kernel weight; WUE, water use efficiency; WUEbm, water use efficiency for biomass; WUEg, water use efficiency for grain.

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“…Transpiration efficiency, canopy temperature, humidity, root growth, and other physiological factors were evaluated in these studies (Bandaru et al, 2006;Reddy, Angira, Blaser, & Stewart, 2015;Thapa et al, 2018) where the majority of the results suggested the clump geometries consistently provided improved microclimates and crop yields were similar or greater than the control (equally spaced plants). Thapa et al (2018) of climate change, specifically increasing temperatures, on crop yields. Two papers published in the Journal of Soil and Water Conservation reported analyses of decades of historical temperature, precipitation, and wheat (Triticum aestivum L.) yields in the U.S. southern High Plains.…”

Section: Research From the Institutementioning

confidence: 99%

“…Figure 1, adapted fromThapa et al (2018), diagrams some of the planting geometries that were tested. Prior to his retirement from the Institute, Dr. Stewart and his graduate students began to investigate the impactF I G U R E 1 Diagram of the horizontal arrangement of corn (or sorghum) plants in various planting geometries tested by Dr. Stewart and colleagues as part of a series of trials performed by the Dryland Agriculture Institute.…”

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confidence: 99%

Dr. B.A. Stewart: Creator and director of the Dryland Agriculture Institute at West Texas A&M University

Blaser

2020

Agronomy Journal

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During his 24 yr as Director of the Dryland Agriculture Institute at West Texas A&M University (WTAMU), Dr. B.A. Stewart had a significant impact on dryland research, undergraduate and graduate students from numerous countries, the faculty and staff at WTAMU, and fellow scientists throughout the soil and crop science world. His influence will continue to have an impact on dryland crop production and researchers for many generations to come.

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Canopy temperature, yield, and harvest index of corn as affected by planting geometry in a semi-arid environment

Cited by 18 publications

References 35 publications

Machine Learning Approach for Prescriptive Plant Breeding

Machine Learning Approach for Prescriptive Plant Breeding

Corn production under restricted irrigation in the Texas High Plains

Dr. B.A. Stewart: Creator and director of the Dryland Agriculture Institute at West Texas A&M University

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