In Defense Of The Extended Logistic Model Of Crop Production

Overman, A. R.; Scholtz, R. V.; Martin, F. G.

doi:10.1081/css-120018979

Cited by 11 publications

(8 citation statements)

References 9 publications

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“…Crop modelling has been gaining momentum over the past decade; partly due to the necessity of sustainable agriculture under environmental degradation along with population growth (Overman and Martin, 2001;Overman et al, 2003;Sepaskhah et al 2011;Du et al, 2014;Camargo et al, 2015;Ramírez-Pérez et al, 2018). Such models help to assess the most appropriate planning in agro-environmental systems, through optimization of water and fertilizer application and finally maximizing returns to farmers.…”

Section: Introductionmentioning

confidence: 99%

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Rafiee

Mahbod

2020

WSA

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Eggplant is an important product in greenhouse cultivation in the world. However, much of its production is done on the farm. Few studies have been conducted on modelling of its growth and yield. Considering the simplicity of the logistic models, only the temperature and dry matter data during the growing season are needed to calibrate them. Hence, we evaluated the performance of the logistic model in growth and yield prediction of eggplant in greenhouse and farm conditions. Eggplants were planted in 2012 and 2013 under different treatments of irrigation and salinity with a complete randomized block statistical design. Irrigation frequency treatments consisted of: daily (I1), weekly, (I2) and every 2 weeks of irrigation (I3). Each pot was irrigated to field capacity level. Four levels of salinity treatments are as follows: electrical conductivities (EC) of 0.8 (J1); 2.5 (J2); 5.0 (J3) and 7.0 (J4) dS∙m-1. The amount of plant dry matter was measured during the growing season (DM) and the amount of product (Y) at the end of the growing season. The logistic model was calibrated with the first-year data and validated with the second-year data. Logistic equation coefficients and harvest index were estimated as a function of the depth and electrical conductivity of irrigation water. The results showed that the accuracy of the logistic model for estimating DM during the growing season was good and predicted the product at the end of the growing season with acceptable accuracy. Also, the model's agreement with the measured DM and Y was good.

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Section: Introductionmentioning

confidence: 99%

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Rafiee

Mahbod

2020

WSA

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“…After assessing several functional forms (hyperbolic, polynomial, and quadratic), a logistic model was selected for the relationship of each response variable to thermal time. This selection was based on model fit criteria, including the Akaike information criterion (AIC), the pseudo-R 2 and percent of variation explained by the model, as well as the ability to interpret biologically each estimated model parameter [25,30,31].…”

Section: Discussionmentioning

confidence: 99%

“…Expolinear growth equations correlated daily solar radiation and air temperature to dry matter accumulation across years [24]. In addition, hyperbolic models related crop yield to competition within wheat [25,26], corn [27], soybeans [28], and potatoes [6]. However, the hierarchical model, expolinear model and hyperbolic models are inherently complex and parameter estimates offer little biological interpretation.…”

Section: Introductionmentioning

confidence: 99%

Modeling Long-Term Trends in Russet Burbank Potato Growth and Development in Wisconsin

et al. 2012

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Improving understanding and prediction of the potato (Solanum tuberosum) tuber size over the growing season is important due to its effects on crop price and marketing. Several models have been proposed to describe potato growth and development, but are based on short-term data and have little use for predicting yields or in-season management decisions. This analysis uses long-term data collected from 1979 to 1993 in central Wisconsin to describe growth and development of the Russet Burbank potato variety. This paper describes average number of potato tubers per plant and tuber length as influenced by thermal time and stem number per plant over 14 years. For each plant variable, data analysis uses multivariate techniques to fit a hierarchical logistic model with parameters potentially depending on stem number per plant. Analysis finds that the average number of potato tubers and average tuber length were affected by thermal time and stem number per plant. Estimated models are biologically relevant, provide an understanding of seasonal thermal variability and stem number per plant effects on average tuber set and growth, and can be used to describe yearly variation in average potato growth

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“…The logistic model, a classic population model of this type [ 23 – 26 ], has been widely used to simulate population growth and has demonstrated a good level of accuracy. Overman et al [ 25 , 27 – 30 ] modelled biomass production in forage grasses over time in a way that accounted for key environmental factors by extending the logistic model to incorporate the timing of harvest and water use efficiency in different years as parameters that were treated as functions of nutrition and water content. Yang et al [ 31 ] incorporated environmental data to develop a dynamic revised logistic model based on fruit growth at different planting densities and seasons for describing the growth of individual tomatoes.…”

Section: Introductionmentioning

confidence: 99%

Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners

Wang

et al. 2015

PLoS ONE

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Simulation models of leaf area index (LAI) and yield for cotton can provide a theoretical foundation for predicting future variations in yield. This paper analyses the increase in LAI and the relationships between LAI, dry matter, and yield for cotton under three soil conditioners near Korla, Xinjiang, China. Dynamic changes in cotton LAI were evaluated using modified logistic, Gaussian, modified Gaussian, log normal, and cubic polynomial models. Universal models for simulating the relative leaf area index (RLAI) were established in which the application rate of soil conditioner was used to estimate the maximum LAI (LAI m). In addition, the relationships between LAI m and dry matter mass, yield, and the harvest index were investigated, and a simulation model for yield is proposed. A feasibility analysis of the models indicated that the cubic polynomial and Gaussian models were less accurate than the other three models for simulating increases in RLAI. Despite significant differences in LAIs under the type and amount of soil conditioner applied, LAI m could be described by aboveground dry matter using Michaelis-Menten kinetics. Moreover, the simulation model for cotton yield based on LAI m and the harvest index presented in this work provided important theoretical insights for improving water use efficiency in cotton cultivation and for identifying optimal application rates of soil conditioners.

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In Defense Of The Extended Logistic Model Of Crop Production

Cited by 11 publications

References 9 publications

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Application of logistic model to estimate eggplant yield and dry matter under different levels of salinity and water deficit in greenhouse and outdoor conditions

Modeling Long-Term Trends in Russet Burbank Potato Growth and Development in Wisconsin

Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners

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