Functional Crop and Cob Growth Models of Maize (<i>Zea mays</i> L.) Cultivars

Prasad, T. V. R.; Krishnamurthy, K. V.; Kailasam, C.

doi:10.1111/j.1439-037x.1992.tb01000.x

Cited by 9 publications

(4 citation statements)

References 8 publications

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“…As observed with the fittings of DMP using fourth degree polynomial here, VENUS and CAUSTON (1979) observed better fit of DMP of sunflower with fourth degree polynomial than Richards function. Similarly in fitting total DMP of the same data in an earlier paper by RAMACHANDRA PRASAD et al (1992), Gompertz followed by Richards in Deccan hybrid and Richards for vegetative and logistic for reproductive stage in Deccan 101 simulated DMP by > 98 % of actual data, based on r^. However, in this paper, polynomials of third and/or fourth degree gave predictions 99.6 % of the actual data with better models predictability, no built in bias and no heteroscedasticity.…”

Section: Deccan 101supporting

confidence: 49%

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Polynomial Functional Models to Simulate Crop Growth in Maize (Zea mays L.) Cultivars

Prasad

Krishnamurthy

Devendra

et al. 1993

J Agronomy Crop Science

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Polynomial and polynomial exponential models of order one to four were tried to describe crop growth in two maize cultivars viz., Deccan hybrid and Deccan 101 of an agronomic trial conducted at the University of Agricultural Sciences, Bangalore, on alfisols. Relative growth rate (RGR) of classical and functional methods was compared. Models performance was assessed based on r2, χ2 residuals sum of squares (RSS), root mean standard deviation (RMSD) and presence or absence of heteroscedasticity in models' predictions. In the cultivars, polynomials of order three and four in Deccan hybrid and cubic polynomial in Deccan 101 simulated dry matter production (DMP) by 99.5 % and showed good distribution of error variance over the observed DMP. The RGR from the third and fourth degree polynomials in both the cultivars was comparable to the classical method and explained better during post‐silking period. Polynomial exponentials predicted very low RGR. Between cultivars, Deccan 101 showed higher RGR during 45–80 days after sowing and contributed significantly more to final DMP.

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Section: Deccan 101supporting

confidence: 49%

“…In the earlier paper of the various sigmoidal functions tested, RAMACHANDRA PRASAD et al (1992) observed that Gompertz followed by Richards in cv. Deccan hybrid and Richards for vegetative and logistic model for reproductive stage in cv.…”

Section: Introductionmentioning

confidence: 98%

Polynomial Functional Models to Simulate Crop Growth in Maize (Zea mays L.) Cultivars

Prasad

Krishnamurthy

Devendra

et al. 1993

J Agronomy Crop Science

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“…This Richards model includes the monomolecular model (δ = 0), von Bertalanffy model (δ = 2 3 ), logistic model (δ = 2) and (by taking the limit as δ → 1) Gompertz equation. A comprehensive Richards function is often used in the quantitative analysis of plant growth; see, for example, [13][14][15][16][17][18]. The Richards function is very flexible and has a horizontal asymptote, and its graph has a characteristic sigmoid shape.…”

Section: Richards Modelmentioning

confidence: 99%

The Application of the Random Time Transformation Method to Estimate Richards Model for Tree Growth Prediction

Cornejo,

Muñoz-Herrera,

Baesler

et al. 2023

Mathematics

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To model dynamic systems in various situations results in an ordinary differential equation of the form dydt=g(y,t,θ), where g denotes a function and θ stands for a parameter or vector of unknown parameters that require estimation from observations. In order to consider environmental fluctuations and numerous uncontrollable factors, such as those found in forestry, a stochastic noise process ϵt may be added to the aforementioned equation. Thus, a stochastic differential equation is obtained: dYtdt=f(Yt,t,θ)+ϵt. This paper introduces a method and procedure for parameter estimation in a stochastic differential equation utilising the Richards model, facilitating growth prediction in a forest’s tree population. The fundamental concept of the approach involves assuming that a deterministic differential equation controls the development of a forest stand, and that randomness comes into play at the moment of observation. The technique is utilised in conjunction with the logistic model to examine the progression of an agricultural epidemic induced by a virus. As an alternative estimation method, we present the Random Time Transformation (RTT) method. Thus, this paper’s primary contribution is the application of the RTT method to estimate the Richards model, which has not been conducted previously. The literature often uses the logistic or Gompertz models due to difficulties in estimating the parameter form of the Richards model. Lastly, we assess the effectiveness of the RTT Method applied to the Chapman–Richards model using both simulated and real-life data.

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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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Functional Crop and Cob Growth Models of Maize (Zea mays L.) Cultivars

Cited by 9 publications

References 8 publications

Polynomial Functional Models to Simulate Crop Growth in Maize (Zea mays L.) Cultivars

Polynomial Functional Models to Simulate Crop Growth in Maize (Zea mays L.) Cultivars

The Application of the Random Time Transformation Method to Estimate Richards Model for Tree Growth Prediction

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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