Dryland and Irrigated Corn Yield with Climate, Management, and Hybrid Changes from 1939 through 2009

Assefa, Yared; Roozeboom, Kraig L.; Staggenborg, Scott A.; Du, Juan

doi:10.2134/agronj2011.0242

Cited by 30 publications

(40 citation statements)

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“…8) are in agreement with previous findings (Assefa and Staggenborg, 2010;Assefa et al, 2012;Schlegel et al, 2016bSchlegel et al, , 2017. The results are also in line with previous findings of Stone et al (1996) and Assefa et al (2014) who concluded grain sorghum yields are greater than corn yields with less than 500 mm of total water available for crop use and corn yields are greater than sorghum yields when total available water supply is above 500 mm.…”

Section: Yield As a Function Of Available Soil Water At Planting Andsupporting

confidence: 92%

Dryland Corn and Grain Sorghum Yield Response to Available Soil Water at Planting

Schlegel

Lamm²,

Assefa

et al. 2018

Agronomy Journal

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 W ater availability at critical growth stages of crops is the major yield limiting factor in dryland crop production (Farahani et al., 1998;Allen, 2012). Dryland agriculture is a special case of rainfed agriculture practiced in arid and semiarid regions. In these regions when irrigation is not used, water conservation becomes the primary focus of management decisions because growing season precipitation is seldom suffi cient to fully meet evapotranspiration demand. Conditions of moderate to severe plant water stress occur during a substantial part of the year and storage of water during fallow for use by a subsequent crop is oft en practiced (Nielsen et al., 2016). Available soil water at planting and ISP are usually the only two sources of water for annual crops such as corn and grain sorghum grown in dryland systems. However, the singular contribution of ASWP for corn and grain sorghum yield was seldom studied.Th e impact of water availability on grain yields of corn and grain sorghum has been studied in depth through yield relationships with ISP or diff erent irrigation levels Payero et al., 2006;Klocke et al., 2011;Assefa et al., 2014;) and with crop water use (Nielsen and Vigil, 2017). Th e infl uence of cropping system on ASWP and on subsequent grain yield has been reported for wheat (Triticum aestivum L.) (Nielsen et al., 2002;Nielsen and Vigil, 2005) and results from these studies indicated that wheat yields were correlated with ASWP. Th e results from Nielsen and Vigil (2005) also suggest that the relationship between ASWP and wheat yield varied year-to-year depending on evaporative demand and ISP. Studies in water-yield relationships of proso millet (Panicum miliaceum L.), pea (Pisum sativum L.), triticale (Tritico secale rimpaui Wittm.), foxtail millet (Setaria italic L. Beauv.), corn, grain sorghum, bean (Phaseolus vulgaris L.), and sunfl ower (Helianthus annuus L.) (Lyon et al., 1995;Felter et al., 2006;Nielsen et al., 2009) suggest that the eff ect of ASWP was greater for short duration crops (bean, proso millet) compared with long duration crops (corn, grain sorghum, sunfl ower).Increase in ASWP due to changes in management practices was among the main factors that contributed to the greater portion of the 139% grain sorghum yield increase in the years 1939 to 1997 (Unger and Baumhardt, 1999 Core Ideas• Yield gains from available soil water at planting decreased as inseason precipitation increased.• Corn yield gain 27 to 33 kg ha -1 (mm available soil water at planting) -1 at in-season precipitation of 196 to 215 mm.• Corn yield gain 9 to 25 kg ha -1 (mm available soil water at planting) -1 as in-season precipitation increased to 288 to 354 mm.• Sorghum yield gain 12 to 22 kg ha -1 (mm available soil water at planting) -1 at in-season precipitation of 163 to 281 mm.• Sorghum yield gain 0 to 6 kg ha -1 (mm available soil water at planting) -1 at in-season precipitation of 315 to 382 mm.

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Section: Yield As a Function Of Available Soil Water At Planting Andsupporting

confidence: 92%

Dryland Corn and Grain Sorghum Yield Response to Available Soil Water at Planting

Schlegel

Lamm²,

Assefa

et al. 2018

Agronomy Journal

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“…Maize grain yield increased from about 1,500 kg/ha in the early 1900s to 8,500 kg/ha at the beginning of the 2000s in the USA (Boomsma et al 2009;Assefa et al 2012). Despite this spectacular achievement, maize grain yield is closely related to plant population density (Van Roekel and Coulter 2011), and the crop suffers from an agronomic weakness of prime significance, affecting its grain productivity and stability.…”

Section: Introductionmentioning

confidence: 99%

Adapting maize crop to climate change

Tokatlidis

2012

Agron. Sustain. Dev.

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Global weather changes compel agriculture to be adequately productive under diverse and marginal conditions. In maize, modern hybrids fail to meet this requirement. Although breeding has achieved spectacular progress in grain yield per area through improved tolerance to stresses, including intense crowding, yields at low plant population densities remain almost unchanged. Stagnated plant yield potential renders hybrids unable to take advantage of resource abundance at lower populations, designating them population dependent. Consequently, the optimum population varies greatly across environments. Generally, the due population increases as the environmental yield potential gets higher. As a remedy, relatively low populations are recommended for low-input conditions leading to inappropriate population in occasional adequacy of resources and considerable yield loss. For example, for a rain-fed hybrid tested at one location across 11 seasons, crop yield potential and optimum population on the basis of the quadratic yield-plateau model varied from 1,890 to 8,980 kg/ha and 4.56 to 10.2 plants/m 2 , respectively, while 100 % yield loss is computed in the driest season if the optimum population for the most favorable season is used. The article reviews the consequences in terms of crop sustainability under widely diverse environments imposed by climatic changes and proposes crop management strategies to address the situation. The major points are: (1) variableyielding environments require variable optimum populations, (2) population dependence is an insurmountable barrier in making a decision on plant population, (3) farmers suffer from considerable yield and income loss, (4) estimating the less population-dependent hybrids among the currently cultivated ones is a major challenge for agronomists, and (5) the development of population-neutral hybrids is a fundamental challenge for maize breeding. Honeycomb breeding is a valuable tool to pursue this goal since it places particular emphasis on the so-far stagnated plant yield potential that is essential for population-neutral hybrid development.

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“…In summary, optimal plant density should be decided based on detailed G ´ E analysis of production conditions that include factors such as CRM, yield productivity environment (weather-soil ´ management practices), and site information. Assefa et al, 2012;Vyn, 2012, 2014) and increase in the number of plants per unit area is suggested as one of the main contributors to yield improvement (Tokatlidis and Koutroubas, 2004;Duvick, 1997;Carlone and Russell, 1987;Tollenaar and Wu, 1999;Ciampitti and Vyn, 2012). Many of these authors claimed increased tolerance to stress (competition) and responsiveness to inputs achieved through breeding as the main reason for increase in plant density.…”

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Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis‐Analysis

et al. 2016

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Identifying an optimal plant density is a critical management decision for corn (Zea mays L.) production. The main objectives of this study were to: (i) investigate the grain yield responses to plant density (yield–density relationship), (ii) identify best fitted yield–density response curves, and (iii) explore genotype (G) × environment (E) interaction effect on yield–density response models. Analysis was conducted on meta‐data (124,374 observations) gathered from 22 US states and 2 Canadian provinces, diverse sites (E), for years from 2000–2014 on multiple hybrids (G). Yield data were further grouped into four yield environments (low [LY], <7 Mg ha−1; medium [MY], 7–10 Mg ha−1; high [HY], 10–13 Mg ha−1; and very high [VHY], >13 Mg ha−1 yielding groups). Primary outcomes from this analysis were: (1) strong G × E interaction; (2) a quadratic model best fitted yield–density relationship; (3) four contrasting yield–density responses identified as dominant in each yield productivity environment, i.e., a declining, a constant, an increasing, and ever‐increasing type; (4) the yield productivity environment varied for the different corn comparative relative maturity (CRM) groups, i.e., the LY environment for long‐maturing hybrids matched with a MY or HY environment for short maturing hybrids; and (5) maximum yielding plant density (MYPD) was lower but maximum yield was greater for long‐ versus short‐maturing hybrids. In summary, optimal plant density should be decided based on detailed G × E analysis of production conditions that include factors such as CRM, yield productivity environment (weather–soil × management practices), and site information.

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Dryland and Irrigated Corn Yield with Climate, Management, and Hybrid Changes from 1939 through 2009

Cited by 30 publications

References 20 publications

Dryland Corn and Grain Sorghum Yield Response to Available Soil Water at Planting

Dryland Corn and Grain Sorghum Yield Response to Available Soil Water at Planting

Adapting maize crop to climate change

Yield Responses to Planting Density for US Modern Corn Hybrids: A Synthesis‐Analysis

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