Leaf area index simulation in soybean grown under near-optimal conditions

Setiyono, T. D.; Weiss, Albert; Specht, James E.; Cassman, Kenneth G.; Dobermann, A.

doi:10.1016/j.fcr.2008.03.005

Cited by 82 publications

(47 citation statements)

References 34 publications

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“…Given the economic importance for Brazil and for the Rio Grande do Sul State and the high speed with which new soybean cultivars are released annually, basic studies that can assist extension workers and consultants to better understand issues related to growth and development of these new cultivars should be performed steadily (Vernetti and Vernetti Junior, 2009). Determining leaf area is important in assessing the photosynthetic efficiency of plants, in determining biotic and abiotic crop damage, in growth analysis studies and for being a great determinant of commercial crop yield (Setiyono et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Estimativa da área de folhas de cultivares antigas e modernas de soja por método não destrutivo

et al. 2014

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The objective of this study was to determine an empirical mathematical model to estimate leaf area of different soybean cultivars from leaf dimensions, with emphasis on modern and currently used soybean cultivars in the Rio Grande do Sul State. A field experiment was conducted at Santa Maria during three growing seasons (2010/2011, 2012/2013 and 2013/2014) Estimativa da área de folhas de cultivares antigas e modernas de soja por método não destrutivoResumo O objetivo do trabalho foi determinar um modelo matemático empírico que estime a área de folhas em cultivares de soja a partir de dimensões lineares da folha, com ênfase para cultivares modernas e recentemente em uso por agricultores no Rio Grande do Sul. Para isso, foi realizado um experimento de campo em Santa Maria com 13 cultivares (NS 4823 RR, BMX Energia RR, Igra RA 518 RR, BMX Turbo RR, NA 5909 RG, TMG 7161 RR Inox, TEC 5936IPRO, IAS 5, BMX Potência RR, Fepagro 36 RR, BRS 246 RR, Bragg e CD 219 RR) durante três anos agrícolas (2010/2011, 2012/2013 e 2013/2014). Foram coletadas 20 folhas de cada cultivar para a calibração do modelo e outras 50 folhas para testar a capacidade preditiva do mesmo, nas quais foi medido o comprimento (C) e a maior largura (L) de cada folíolo central dos trifólios. A partir da relação entre área da folha e as dimensões lineares do folíolo central foi ajustado um modelo linear para cada cultivar e um modelo geral para todas as cultivares. A capacidade preditiva das equações foi avaliada pelas estatísticas raiz quadrada média do erro, BIAS, índice de concordância modificado e coeficiente r. Os resultados indicam que o método não destrutivo, baseado nas dimensões lineares do folíolo central, é apropriado para a estimativa da área de folhas individuais em soja e que a equação geral AF = 2,0185.(C.L) pode ser utilizada para várias cultivares modernas de soja.Palavras-chave: Glycine max, índice de área foliar, modelos matemáticos, dimensões lineares.

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Section: Introductionmentioning

confidence: 99%

Estimativa da área de folhas de cultivares antigas e modernas de soja por método não destrutivo

et al. 2014

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“…According to Yang et al (2011), dry accumulation of cotton during the season could be described by the logistic curve. In addition, the logistic curve was used to describe changes in the soybean leaf index (Setiyono et al, 2008). Moustakas and Ntzanis (2005) stated that the accumulation of dry matter in the leaves, stem, root and aboveground parts of the tobacco plant during the growing season followed a typical sigmoidal pattern.…”

Section: Resultsmentioning

confidence: 99%

Effect of sowing date on dry matter accumulation in two-rowed winter barley

Mirosavljević¹,

Momčilović²,

Maksimović³

et al. 2018

Selekcija i semenarstvo

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Information about dry matter accumulation is of great importance for further increase of barley yield. Various nonlinear and linear curves have been used to describe vegetative and reproductive growth for a number of agricultural crops. Although dry matter accumulation is an important parameter of crop growth, knowledge about biomass production under agro-ecological conditions of the Pannonian Plain are scarce. This study was undertaken to analyze the pattern of dry matter accumulation in two-rowed winter barley cultivars and changes in biological parameters of the logistic curve under different sowing dates and growing seasons. Therefore, trials with two late cultivars of two-rowed winter barley were conducted in two growing seasons and four sowing dates. Results from this study indicate that dry matter accumulation in both growing seasons followed a typical sigmoid shape. Delayed sowing led to a significant decrease in the maximum value of plant dry weight, the termal time needed to reach maximum value and duration of exponential growth, and increase in maximum crop growth rate. These results indicate that apart from appropriate cultivar selection, early barley sowing is also important to achieve high plant dry matters one of the main determinants of grain yield.

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“…In YP model using only LAI, errors in predicted yield would result from the changes in specific leaf area (SLA) in a given season and region. The SLA is the ratio of leaf area to leaf biomass [50] and is affected by environmental conditions [51], such as weather and disease [52]. As a result, the SLA value differs by season even when the same crops are cultivated at a given site [7].…”

Section: Discussionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

Ban

Kim

Park

et al. 2016

Preprint

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data product, which are 8-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season [end of DOY (EOD)] was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield; the yield model (YP) used LAD from predicted emergence to maturity, and the yield model (YF) used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed a similar or better performance, even for the much earlier yield prediction at DOY 257, compared to that of the YF model. In conclusion, the phenology and yield models were developed based only on logistic changes in remote sensing-derived LAD, and predicted phenological dates and corn yields with considerable accuracy and precision for the two regions selected for this study. However, these models must be examined for spatial portability in more diverse agro-climatic regions.

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Leaf area index simulation in soybean grown under near-optimal conditions

Cited by 82 publications

References 34 publications

Estimativa da área de folhas de cultivares antigas e modernas de soja por método não destrutivo

Estimativa da área de folhas de cultivares antigas e modernas de soja por método não destrutivo

Effect of sowing date on dry matter accumulation in two-rowed winter barley

Using MODIS Data to Predict Regional Corn Yields

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