A comparison of crop production functions using simulated data for irrigated corn in western Kansas

Llewelyn, Richard V.; Featherstone, Allen M.

doi:10.1016/s0308-521x(96)00080-7

Cited by 75 publications

(46 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The methodology has been applied to wheat, cotton, maize and sugar beet by Hexem and Heady (1978), Vaux and Pruitt (1983), Gibbons (1986) or Llewelyn and Featherstone (1997).…”

Section: Methodsmentioning

confidence: 99%

Estimating marginal value of water for irrigated olive grove with the production function method

Mesa-Jurado¹,

Berbel²,

Orgaz

1970

Span. j. agric. res.

View full text Add to dashboard Cite

Economic valuation of irrigation water is done through the use of production functions for the case of the olive grove. In order to do so the integration of an agronomic model (based on the production function) and an economic model linked to the profitability of the crop (the ratio of revenue and operating costs) in the area under study is proposed. The study case encompasses the Guadalbullon River Sub-basin area, belonging to the Guadalquivir River Hydrologic Demarcation (Southern Spain). Within the overall deficit of the Guadalquivir River basin, the Guadalbullon River poses a special problem as it is unregulated and there are important irrigated fields on its banks, most of them olive groves. Net marginal value of water obtained (having deducted the variable costs of production including harvesting and irrigation) is€ 0.60 m–3 for the allocation of 1,000 m3 ha–1 and € 0.53 m–3 for the water right allowance of 1,500 m3 ha–1 (average for period 2005/2008). The results obtained support the recommendation by other authors suggesting the use of deficit irrigation in olive, additionally the high value of water estimated contributes to explain the substantial increase in irrigated olive area in Andalucia.

show abstract

“…The methodology has been applied to wheat, cotton, maize and sugar beet by Hexem and Heady (1978), Vaux and Pruitt (1983), Gibbons (1986) or Llewelyn and Featherstone (1997).…”

Section: Methodsmentioning

confidence: 99%

Estimating marginal value of water for irrigated olive grove with the production function method

Mesa-Jurado¹,

Berbel²,

Orgaz

1970

Span. j. agric. res.

View full text Add to dashboard Cite

show abstract

“…This extends the literature on the comparison of different functional forms [e.g. [1][2][3][4][5] by taking the effect of outliers for the estimation and evaluation of crop production functions into account.…”

Section: Introductionmentioning

confidence: 62%

“…These functional forms are frequently used in the literature and proved to accurately capture the underlying relationships [1,[4][5][6][7][8][9][10][11][12].…”

Section: Production Functionsmentioning

confidence: 99%

“…So far, comparison of functional forms has been based on the coefficient of determination [2], residual distribution [3], non-nested hypothesis testing [4] and potential misspecification costs [5], respectively. Using LS and robust regression, we devote special attention to the cost of misspecification which constitutes an economic approach to the comparison of production functions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Application of Robust Regression to a Production Function Comparison~!2008-08-21~!2008-11-10~!2008-11-28~!

Finger¹,

Hediger²

2008

TOASJ

View full text Add to dashboard Cite

Abstract:The adequate representation of crop response functions is crucial for agronomic as well as agricultural economic modeling and analysis. So far, the evaluation of such functions focused on the comparison of different functional forms. In this article, the perspective is expanded also by considering different regression methods. This is motivated by the fact that exceptional crop yield observations (outliers) can cause misleading results if least squares regression is applied. In order to address this problem we also apply robust regression techniques that are not affected by such outliers. We evaluate the quadratic, the square root and the Mitscherlich-Baule function using the example of Swiss corn (Zea mays L.) yields. It shows that the use of robust regression narrows the range of optimal input levels across different functional forms and reduces potential costs of misspecification compared to least squares estimation. Thus, differences between functional forms are reduced by applying robust regression.

show abstract

“…We assumed that farmers set their irrigation schedules prior to planting, when weather is unknown, but the actual water applied depends on the actual weather in the growing season. This is an important distinction from previous studies that assumed the choice of seasonal irrigation depth was fixed, so farmers were restricted to applying the same amount of water in dry and wet years (Llewelyn and Featherstone, 1997;O'Brien et al, 2001;Peterson and Ding, 2005).…”

Section: Aquacrop and Irrigation Schedulingmentioning

confidence: 99%

Using a Crop Simulation Model to Understand the Impact of Risk Aversion on Optimal Irrigation Management

Wibowo¹,

Hendricks²,

Kisekka³

et al. 2017

Transactions of the ASABE

View full text Add to dashboard Cite

A comparison of crop production functions using simulated data for irrigated corn in western Kansas

Cited by 75 publications

References 15 publications

Estimating marginal value of water for irrigated olive grove with the production function method

Estimating marginal value of water for irrigated olive grove with the production function method

The Application of Robust Regression to a Production Function Comparison~!2008-08-21~!2008-11-10~!2008-11-28~!

Using a Crop Simulation Model to Understand the Impact of Risk Aversion on Optimal Irrigation Management

Contact Info

Product

Resources

About