A wheat grazing systems model for the US Southern Plains: Part II—Economic analysis of grazing management decisions under weather uncertainty

Rodríguez, Abelardo; Trapp, James N.; Walker, Odell L.; Bernardo, Daniel J.

doi:10.1016/0308-521x(90)90070-7

Cited by 10 publications

(6 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regional or whole-system studies are also common in agriculture, and include models of the grain distribution system of the USA (Koo and Thompson 1982), the dairy industry of north-eastern USA (Pratt et al 1986), animal production in the savannas of Colombia (Thornton 1988), the wheat grazing systems of the USA southern plains (Rodriguez et al 1990), the northern Australian grazing industry (White et al 1998), and fisheries in Western Australia (Watson and Sumner 1999) and the English Channel (Mardle and Pascoe 2000). Agricultural models covering whole nations (or even the global level) remain possible, but would tend to be difficult to implement properly because of the diverse and heterogeneous nature of any targeted system on this scale.…”

Section: Features and Methodologiesmentioning

confidence: 99%

“…Whilst a comprehensive evaluation method, it is unworkable with even moderately-sized problems, and the only agricultural literature examples are from models with a small or deliberately simplified searchspace. Rodriguez et al (1990) evaluated eleven stocking densities by nine time combinations in a grazing systems model of the southern USA, and Gates et al (1994) considered discrete temperature settings for a controlled-environment piggery model. A seven-way factorial of a dairy genetic improvement model, with a total of 640 discrete combinations, was subjected to analysis of variance (Mayer et al 1994b).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary Algorithms and Agricultural Systems

Mayer¹

2002

View full text Add to dashboard Cite

Appendix 1 79References 89Index 105 PrefaceSystems research is increasingly being used to investigate and analyse a wide range of real-world problems, including agricultural production systems. Given a valid, verified model of a particular system, optimisation is a logical complement to the modelling exercise. Usually, this takes the form of maximisation of some measure of the system's performance, such as total production or economic gross margin. This book deals with the practical application of optimisation techniques, particularly evolutionary algorithms, to the study and management of these agricultural systems. It should prove useful to practitioners applying these methods to the optimisation of agricultural or natural systems, and would also be suited as a text for systems management, applied modelling, or operations research university subjects. Basic knowledge in systems research, along with some computing and programming skills, are assumed.Models of agricultural systems range widely on both temporal and spatial scales. Farm-level systems have typically been investigated, but models also range out to regional, industry and national scales. Short-term (within-year) profitability and cash-flow issues are common, but the time-frame can be extended to a hundred years or more, to investigate sustainability and long-term effects. In addition to the 'direct' economic maximisation of agricultural systems, optimisation methods have also seen use in the calibration of internal model parameters to observed data, maximising the rate of genetic gain in livestock, in agricultural allocation and scheduling problems, and in the analysis of sustainability issues in natural systems management.Agricultural models present a number of difficulties with regard to optimisation. These problems include complex relationships which are not conducive to the simpler forms of economic modelling (such as linear programming); biological variability, which usually requires a stochastic viii model; the identification of suitable variables to optimise; the high degree of complexity in these systems, which translates to high dimensionality of the search-space; frequent interactions between the effects of the various (assumedly independent) management options; cliffs and discontinuities in the search-space (where the system is over-utilised, and 'crashes' both biologically and economically); and the presence of multiple local optima, caused by very different combinations of management options having similar economic outcomes.Any selected optimisation method is required to deal with all these problems, and reliably return the solution for the global optimum (or a value suitably close to this). Generally, evolutionary algorithms (including genetic algorithms, evolution strategies, and hybrid methods) have proven superior for this task. Depending on the algorithm and the type and usage of the model, some problems do remain, however evolutionary algorithms contain a number of advantageous features which largely circumvent these. For the altern...

show abstract

Section: Features and Methodologiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms and Agricultural Systems

Mayer¹

2002

View full text Add to dashboard Cite

show abstract

“…Mauget et al (2009) reported that optimal SR varied with climate forecasts and prevailing prices of wheat grain and livestock. Rodríguez et al (1990b) pointed out that optimal SR depended on specifi c climatic and managerial conditions, cattle prices, and producers' risk preference. Considering climate variability, they concluded that risk-averse producers should stock at low SR, while risk lovers stock at high SR. Kaitibie et al (2003a) estimated optimal SR with a statistical model using a linear-response stochastic plateau function for ADG production developed based on the 1992-2000 grazing data of Kaitibie et al (2003b).…”

Section: Economic Optimum Stocking Ratementioning

confidence: 99%

Optimizing Stocking Rate for Maximum Return to a Wheat–Cattle Enterprise Using Model Simulation and Economics

Zhang

2010

Agronomy Journal

View full text Add to dashboard Cite

Managing dual-purpose wheat is complex because of the tradeoff relationship between cattle (Bos taurus) and wheat (Triticum aestivum L.) production. Stocking rate (SR) and planting date are key decision variables of the dual systems. Th e objective was to develop decision support information that help farmers boost profi ts by adapting SR and planting date to particular production and market conditions. A wheat grazing model was used to simulate the systems for seven SR (0-3 head ha-1), fi ve planting dates, three climate scenarios, and two initial soil moisture profi les. Net returns were estimated for three representative markets. Net returns under optimized management doubled those under the business-as-usual for the comparable planting dates, indicating great potentials to boost net returns with better management. Optimal SR depends on markets and forage availability. For market that overwhelmingly favors grain production, grain-only wheat should be used; whereas dual-purpose wheat is preferred under other market conditions. For wet soil at planting, maximum returns are attained in early September planting at high SR of > 2 head ha-1 despite climates; however, the high optimal SR should be adjusted down with delayed planting in wet and average but maintained in dry climate for water conservation. For dry profi le at planting, optimal management is dual-purpose wheat with SR of 3 in wet, grainonly wheat in average, and fallow in dry climate. Had wheat been established in severely dry years, moderate grazing at 0.5 to 1.5 SR could reduce net losses. Under most circumstances, grazing should not be allowed to pass fi rst hollow stem to maximize returns.

show abstract

“…It is harvested annually on more than 220 million hectares of cropland globally, making it the most widely grown crop in the world. It also has a long and varied growing system that spans a wider range of conditions than other crops: most wheat is grown as winter wheat, whereby it is planted in late fall, spends cold winter months dormant, and then emerges, grows, and ripens in spring (Hu et al., 2005) Wheat can also be used directly as forage to graze cattle, a practice known in the United States as backgrounding (Fieser et al., 2006; Hossain et al., 2004; Rodriguez et al., 1990). This means that its value as food versus fodder could influence farmer harvest decisions.…”

Section: Introductionmentioning

confidence: 99%

Temperature‐driven harvest decisions amplify US winter wheat loss under climate warming

Zhu

Burney

2020

Global Change Biology

View full text Add to dashboard Cite

Most studies quantifying the impacts of climatic variability and warming on crop production have focused on yields and have overlooked potential areal and frequency responses, potentially biasing future projections of food security in a warming world. Here we analyze US winter wheat production from 1970 to 2017 and find that harvest area ratio (harvested area/planted area, HAR) has declined while yields have risen, standing in stark contrast to other US staple crops. Although lower profitability due to declining wheat prices appears to explain the HAR trend, fluctuating wheat yields—largely explained by temperature exposure—drive the interannual variation of HAR. Our analysis suggests that warming‐induced declines in HAR are comparable in magnitude to heat‐related yield losses, and lower wheat prices amplify the sensitivity of HAR to warming and yield variation. Although irrigation mitigates some temperature‐driven yield effects, it does little to change HAR, likely due to infrastructure cost and limited influence on relative profitability. Our results suggest that an accurate quantification of climate impacts on crop production must account for harvested area response, and that future adaptation strategies should not only target crop choice and management but also harvest incentives.

show abstract

A wheat grazing systems model for the US Southern Plains: Part II—Economic analysis of grazing management decisions under weather uncertainty

Cited by 10 publications

References 6 publications

Evolutionary Algorithms and Agricultural Systems

Evolutionary Algorithms and Agricultural Systems

Optimizing Stocking Rate for Maximum Return to a Wheat–Cattle Enterprise Using Model Simulation and Economics

Temperature‐driven harvest decisions amplify US winter wheat loss under climate warming

Contact Info

Product

Resources

About