Mathematical Models of Crop Growth and Yield

Overman, A. R.; Scholtz, R. V.

doi:10.1201/9780203909225

Cited by 36 publications

(9 citation statements)

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“…These include plant population, level of applied nutrients, water availability (such as rainfall or irrigation), and frequency of harvest (for perennial grasses). Some of these interactions have been detailed in Overman and Scholtz [1] & [2] .…”

Section: Discussionmentioning

confidence: 99%

“…The second step is to utilize a mathematical model which relates biomass accumulation to calendar time. For this purpose we adopt the expanded growth model discussed in Section 3.5 of Overman and Scholtz [2] , which can be written as where A is a yield factor, Mg ha −1 ; and Q is a dimensionless growth quantifier , defined by in which the dimensionless time variable x is defined by with the parameters in Eq. (3) defined as time to the mean of the solar energy distribution (referenced to Jan. 1 for the northern hemisphere), wk; the time spread of the solar energy distribution, wk; k the partition coefficient between light-gathering and structural components of the plants, and c an aging coefficient for the plant species, wk −1 .…”

Section: Methodsmentioning

confidence: 99%

“…The model described the data very well and exhibited similarities among the three studies. In a textbook the authors have discussed various aspects of crop growth and yield [2] , including a mathematical model of crop growth with calendar time. The expanded growth model incorporates the three basic processes of an energy driving function, partitioning of biomass between light-gathering and structural components of the plants, and an aging function.…”

Section: Introductionmentioning

confidence: 99%

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Accumulation of Biomass and Mineral Elements with Calendar Time by Corn: Application of the Expanded Growth Model

Overman

Scholtz

2011

PLoS ONE

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The expanded growth model is developed to describe accumulation of plant biomass (Mg ha−1) and mineral elements (kg ha−1) in with calendar time (wk). Accumulation of plant biomass with calendar time occurs as a result of photosynthesis for green land-based plants. A corresponding accumulation of mineral elements such as nitrogen, phosphorus, and potassium occurs from the soil through plant roots. In this analysis, the expanded growth model is tested against high quality, published data on corn (Zea mays L.) growth. Data from a field study in South Carolina was used to evaluate the application of the model, where the planting time of April 2 in the field study maximized the capture of solar energy for biomass production. The growth model predicts a simple linear relationship between biomass yield and the growth quantifier, which is confirmed with the data. The growth quantifier incorporates the unit processes of distribution of solar energy which drives biomass accumulation by photosynthesis, partitioning of biomass between light-gathering and structural components of the plants, and an aging function. A hyperbolic relationship between plant nutrient uptake and biomass yield is assumed, and is confirmed for the mineral elements nitrogen (N), phosphorus (P), and potassium (K). It is concluded that the rate limiting process in the system is biomass accumulation by photosynthesis and that nutrient accumulation occurs in virtual equilibrium with biomass accumulation.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accumulation of Biomass and Mineral Elements with Calendar Time by Corn: Application of the Expanded Growth Model

Overman

Scholtz

2011

PLoS ONE

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“…The values of linear parameters Y m and y o should depend upon crop species, applied nutrients, and water availability. Effects of some of these factors on crop yields have been discussed elsewhere [13] .…”

Section: Discussionmentioning

confidence: 99%

Model of Yield Response of Corn to Plant Population and Absorption of Solar Energy

Overman

Scholtz

2011

PLoS ONE

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Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha−1 and g plant−1) on plant population (plants m−2). Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L.) grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Ym (Mg ha−1) for maximum yield at high plant population and c (m2 plant−1) for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, xc = 1/c (plants m−2). The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of xc were very similar for the three field studies with the same crop species.

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“…Logistic model has been widely applied in predicting the growth, dry matter accumulation and yield of many crops such as maize, barley, and Solanaceous vegetable like tomato (Prasad and Kailasam, 1992;Overman and Scholtz, 2002;Karadavut et al, 2010;Sari et al, 2019). However, its application to Brassica vegetables has not been reported.…”

Section: D Time-based Growth Model and 3d Light-time-biomass Response Model Of Choy Sum Seedlingmentioning

confidence: 99%

Light-Time-Biomass Response Model for Predicting the Growth of Choy Sum (Brassica rapa var. parachinensis) in Soil-Based LED-Constructed Indoor Plant Factory for Efficient Seedling Production

2021

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Little is known about how exactly light plays its role in the growth of choy sum (Brassica rapa var. parachinensis), a widely cultivated vegetable in Asia. By applying a commercial soil using black peat as major constituent with 17:10:14 ratio of NPK fertilizer in this study, the growth responses of choy sum seedling to progressively increasing white LED light intensity in an indoor plant factory were investigated, where positive enhancements were observed in choy sum morphology and growth including both dry and fresh mass accumulation under higher light intensity till 400 μmol/(m2⋅s), then a reduction occurred due to light oversaturation and overheat. In indoor plant factory, the inhomogeneous distribution phenomenon of illumination level was inevitably occurred in indoor farm racks generally. For accurately evaluating the productivity of choy sum grown on such racks, a light-time-biomass response model of choy sum seedling grown at the seedling stage was thus established for the first time, which could reliably predict the production outcome of this species in indoor farming practice under various lighting condition and duration. The robustness of the model was further tested by model variation test and sufficient robustness of this model was confirmed. The new insight obtained for the light-dependence of choy sum growth and the light-time-biomass response model can be used to efficiently direct its seedling production in indoor plant factories.

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Mathematical Models of Crop Growth and Yield

Cited by 36 publications

References 0 publications

Accumulation of Biomass and Mineral Elements with Calendar Time by Corn: Application of the Expanded Growth Model

Accumulation of Biomass and Mineral Elements with Calendar Time by Corn: Application of the Expanded Growth Model

Model of Yield Response of Corn to Plant Population and Absorption of Solar Energy

Light-Time-Biomass Response Model for Predicting the Growth of Choy Sum (Brassica rapa var. parachinensis) in Soil-Based LED-Constructed Indoor Plant Factory for Efficient Seedling Production

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