A test of a maize growth and development model, CORNF

Wright, Allen D.; Keener, M. E.

doi:10.1016/0308-521x(82)90019-1

Cited by 7 publications

(7 citation statements)

References 8 publications

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“…The range in topsoil depths creates environments with different soil water holding capacities and thus differences in the occurrence of crop water deficit stress. Previous experiments indicate that flowering maize (Zea mays L.) plants in the 0.3-m plots, exhibit a gradient of water-deficit-stress symptoms within 8 d after a rain or irrigation (Griffin, 1980;Wright and Keener, 1982). More recently, experiments conducted on this field revealed differences in soybean [Glysine max (L.) Merr.]…”

Section: Experimental Design and Crop Managementmentioning

confidence: 90%

“…Field experiments were conducted in 2009 and 2010 at the Bradford Research and Extension Center near Columbia, MO (38°53¢38.82² N, 92°12¢5.06² W), on a field modified to restrict rooting to a range of different depths (Griffin, 1980;Wright and Keener, 1982). The site was modified in 1978 by excavating a series of 61-m-long and 7.5-m-wide channels that were lined with plastic and drain tile and then refilled with topsoil (Fine, smectitic, mesic Vertic Epiaqualfs).…”

Section: Experimental Design and Crop Managementmentioning

confidence: 99%

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Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Hoyos‐Villegas

Fritschi

2013

Crop Science

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2631ReseaRch Y ield is influenced directly and indirectly by a number of factors, such as plant morphology and physiology, and especially environmental conditions (Prasad et al., 2007a). A good understanding of the factors responsible for growth and development is required to identify an indirect selection tool that can be used to improve yield (Richards, 1982). Reynolds et al. (2001) emphasized the potential of using different morphophysiological selection criteria to complement empirical selection for yield, which potentially can make the selection process more efficient.Digital image analysis is an important tool in biological research and has been applied to satellite images, aerial photographs, and macroscopic and microscopic images. A relevant application of image analysis that has been used for decades is the area of remote sensing. In agricultural and forestry systems, analyses including species identification and estimation of plant cover area and biomass of the above-ground canopy have been conducted using satellite and/or airborne images (Klassen et al., 2003). Image analysis techniques have found a recent application in estimating the biomass of individual plants in controlled ABSTRACT Improving crop productivity in drought-prone environments is a daunting challenge. Selection of advanced breeding materials for yield is a labor-intensive procedure and sometimes produces misleading results because of the complex genetic behavior of yield. remote sensing techniques can provide an instantaneous, nondestructive, and quantitative assessment of a crop's ability to intercept radiation and photosynthesize. The objective of this study was to examine vegetation indices derived from aerial images as biomass and yield prediction tools for soybean [Glycine max (L.) Merr.] under different levels of water availability. Two commercial soybean cultivars with contrasting maturity were planted on a rooting-depth restriction installation. Multispectral aerial images were acquired at early flowering and during seed filling, and fifteen vegetation indices were calculated and their associations with yield and biomass assessed. The indices estimated using the near infrared (NIr), rED, and GrEEN portions of the spectrum were weak predictors of soybean yield under severe water stress conditions. However, under moderate drought or unstressed conditions, the regressions were able to explain up to 80% of the data on the basis of R 2 values. The nominally best relationships with yield were found for NIr from images taken at seed fill and with biomass for rED bands extracted from images taken at flowering. results suggest that aerial imaging shows potential as a tool for yield and biomass prediction of soybean cultivars.

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Section: Experimental Design and Crop Managementmentioning

confidence: 90%

Section: Experimental Design and Crop Managementmentioning

confidence: 99%

Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Hoyos‐Villegas

Fritschi

2013

Crop Science

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“…Furthermore, the process of leaf {:xpansion is known to be influenced by even small waller deficits (Boyer, 1968(Boyer, , 1970Bunce, 1977). Therefore, although water deficit has little effect on rate of lf:af emergence or ligule development Dale, 1980, Wright andKeener, 1982;Bennett and Hammond, 1983), it significantly reduces total leaf area through reduced leaf expansion and increased senescence rates (Wilson and Allison, 1978;Bennett a:nd Hammond, 1983).…”

Section: ----------------mentioning

confidence: 99%

Leaf Area Development in Field‐Grown Maize¹

Dwyer¹,

1986

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Leaf area is important for crOJI light interception and therefore has a large influence on crop yield. In this study, a method was devised to characterize and predict the development process of maize (Zea mays L.) leaf area by separating the process into time of appearance of each mature leaf (leaf !itage) and leaf area of each mature leaf (leaf expansion). Leaf area was measured for several years on each of two soil types (a sandy loam of the Uplands association (Typic Haplorthod) and a clay of the Dalhousie association (Typic Haplaquoll)J at Ottawa, Canada. Analysis of field data indicated that leaf stage was highly correlated with growing degree days (base temp of l0°C} accumulated from planting (r = 0.98). However, air temperature alone did not account for the annual variability in leaf area of mature leaves. To separate leaf expansion from leaf stage, mature area per leaf was plotted as a function of leaf number. The resultant curve had a slightly skewed bell shape whose amplitude represented the leaf area of the largest leaf. When these curves were normalized with respect to their amplitudes they varied little from year to year. The amplitudes compared well (r=0.87) to plant-available water averaged from planting to development of the largest leaf and the sum of minimum daily temperature for the same period. Total mature plant leaf area for each year was then calculated by multiplying the integration of the normalized bell shaped curve times the amplitude calculated by the regression equation. These calculated total mature plant leaf areas were used to normalize total plant leaf areas (expanding plus mature leaf area in the absence of senescence). The normalized plant leaf areas were expressed as an Sshaped logistic function of growing degree days and total plant leaf areas were calculated over each growing season. An additional relationship related water deficit during growth to senescence and the amount of leaf area senesced was subtracted from total plant leaf areas to obtain actual plant leaf area. Estimates of actual plant leaf area for the six growing seasons used to develop the method compared well with measured values (r between 0.94 and 0.99). Estimates of actual plant leaf area for two independent years at the same location were also good (r=0.91 and 0.97). This approach reduces prediction of maize leaf area to relatively simple functions of temperature and available water.----------------Additional index words: Corn, Zea mays L., Plant water stress, Leaf expansion, Crop growth, Phenological development.

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“…The CERES-Maize models generally under-measured kernel weight, but with slope significantly estimated kernel numbers, and unlike the response ob-greater than one ( Table 2). The VO overestimated tained by Wright and Keener ( 1982), the grain number (bias of 48 mg/kemel) and SAT and CORNF under-estimated kernel weight (bias of -74 and -59 mgj kernel, respectively) ( Table 2). Linear regression coefficients for simulated versus measured corn grain yield were not significant (P < 0.05).…”

Section: Phenologymentioning

confidence: 98%

Suitability of Corn Growth Models for Incorporation of Weed and Insect Stresses

Retta¹,

Vanderlip²,

Higgins³

et al. 1991

Agronomy Journal

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Shattercane [Sorghum bicolor (L.) Moench] and second generation European corn borer (ECB) [Ostrinia nubililis (Hubner)] are pests that singly or in combination reduce corn (Zea maize L.) production in the northcentral regions of the USA. Shattercane reduces corn growth and yield because it competes effectively with corn for light and water. Second generation ECB larvae, in tunneling through the vascular system, apparently affect yield by disrupting water and photosynthate movements. Pest models may be linked to physiological models for assessing the effects of pest stresses on corn growth and yield. CERES‐Maize and CORNF corn growth models were chosen to test accuracy and consistency in predicting corn growth and yield parameters. The objectives were to evaluate corn growth models to which pest models could be attached and to test the sensitivity of the selected model to variations in light and water. Simulated leaf area index; vegetative, grain, and total biomass; and yield components were compared to measured data. CERES‐Maize modified for leaf growth and phenology computations (VO/SAT) gave more accurate predictions of date of silking (bias = 1 d) than CORNF (bias = 6 d) or original version CERES‐Maize (bias = −5 d). Accurate estimation of phenology is important because the severity of yield reduction from ECB infestation is dependent on the stage of growth. Sensitivity of VO/SAT to reductions in light and water inputs was tested by simulating combinations of light and water levels ranging from 50 to 100% of actual. A 50% reduction in light resulted in average reductions of 26% in yield, 16% in kernel weight, 16% in kernel number, and 20% in leaf area index. Similarly, a 50% reduction in precipitation resulted in average reductions of 47% in yield, 51% in kernel weight, 1% in kernel number, and 1% in leaf area index. The combination model showed adequate sensitivity to light and water, and thus could be modified to mimic weed competition.

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A test of a maize growth and development model, CORNF

Cited by 7 publications

References 8 publications

Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Leaf Area Development in Field‐Grown Maize¹

Suitability of Corn Growth Models for Incorporation of Weed and Insect Stresses

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A test of a maize growth and development model, CORNF

Cited by 7 publications

References 8 publications

Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Relationships Among Vegetation Indices Derived from Aerial Photographs and Soybean Growth and Yield

Leaf Area Development in Field‐Grown Maize1

Suitability of Corn Growth Models for Incorporation of Weed and Insect Stresses

Contact Info

Product

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Leaf Area Development in Field‐Grown Maize¹