Optimal harvest policies for corn and soybeans

Morey, R. Vance; Peart, R. M.; Zachariah, G. L.

doi:10.1016/s0021-8634(72)80001-5

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…For grains, models were developed by Donaldson (1968), Morey et al (1972), Philips and O'Callaghan (1974), Fokkens and Puylaert (1981) and Deris and Ohta (1990); for sugar cane the models were developed by Guise and Ryland (1969), Crane et al (1982), Balastreire (1987), Gualda and Tondo (1991), Barata (1992). Another analogous application can be found in forest management problems, with examples been documented by Dias et al (1984), Chaudhuri and Sem (1987), Garcı´a (1990) and Bettinger et al (1997).…”

Section: Methods For Harvesting Schedulingmentioning

confidence: 99%

Orange harvesting scheduling management: a case study

Caixeta-Filho

2006

Journal of the Operational Research Society

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The competitiveness of Brazil's citrus sector is a function of quality control in the transformation of fruit into juice. The transformation process commences with the harvest, the timing of which significantly affects fruit quality. In this paper, a mathematical model is formulated that links pertinent chemical, biologic, and logistic restrictions to the quality of the fruit to be harvested, applying linear programming theory. The modelling structure was verified and validated with real data from 320 Brazilian farms involved with an annual production of approximately 7 200 000 boxes of oranges. It could be attested that the maximization of the number of boxes of oranges to be harvested (strategy that is still adopted by a representative number of Brazilian citrus farmers, based on the industry advice) does not necessarily correspond to the maximum quantity of total soluble solids (TSS). In many cases, citrus harvested at the optimum TSS point offered higher concentrated juice productivity. The estimated potential benefits ($) from using the proposed model reached figures over 6%.

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Section: Methods For Harvesting Schedulingmentioning

confidence: 99%

Orange harvesting scheduling management: a case study

Caixeta-Filho

2006

Journal of the Operational Research Society

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“…Mathematical programming models of harvest systems (Krutz, Combs and Parsons 1980;Morey, Peart and Zachariah 1972) generally assume that weather is a deterministic variable and the optimal machinery set is determined assuming perfect information. While mathematical programming models can find an optimal machinery set, the variability in returns associated with that machinery set is not known because weather is treated as a deterministic variable.…”

Section: Review Of Related Literaturementioning

confidence: 99%

The Effect of Producers' Risk Attitudes on Sizing the Harvest and On‐farm Drying System

Davis

Patrick

2002

Canadian J Agri Economics

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A simulation model of an on‐farm grain harvest and drying system was used to determine net returns associated with alternative sized systems. The physical processes of corn and soybean planting, growth, harvest and drying were simulated under historical weather conditions for 33 years. Weather data determined the number of field days, physiological maturity date, harvest grain moisture, yield losses and dryer energy costs. Guidelines developed by agricultural engineers and agricultural economists were used to initially size the combine and dryer for three sizes of farms. Capacities of the combine and dryer were increased and decreased by 10, 20, 30, 40 and 50%, independently. Net returns to labor and management, reflecting yield and price correlations, were determined for each of the equipment combinations. Stochastic dominance was used to evaluate the distributions of net revenue and determine the risk efficient combine and dryer sets. It was found that there were small differences in net returns for systems that completed harvest and drying in the 27B32 day range for all three farm sizes. For a given size of farm, risk averse producers were found to prefer smaller capacity combines and dryers. Les auteurs se sont servis d'un modèle simulant la récolte et le séchage du grain à la ferme pour calculer le revenu net de systèmes de production de taille variable. Les aspects physiques de la plantation, de la croissance, de la récolte et du séchage du maïs et du soja ont été reproduits en fonction des données météorologiques relevées pendant 33 ans. Ces données ont permis d'établir le nombre de jours de croissance, la date de la maturité physiologique, la teneur en eau du grain, les pertes de rendement et le coût de l'énergie nécessaire au séchage. Ensuite, les auteurs ont suivi les recommandations des agronomes et des économistes agricoles pour déterminer les dimensions initiales de la moissonneuse et du séchoir pour trois exploitations de taille différente. La capacité de la moissonneuse et celle du séchoir ont été augmentées ou réduites indépendamment de 10, de 20, de 30, de 40 et de 50 pour cent, puis on a calculé le revenu net venant du travail et de la gestion du producteur en tenant compte de la corrélation entre le rendement et les prix pour chaque combinaison. La dominance stochastique a permis d'estimer la distribution du revenu net et d'identifier les ensembles moissonneuse/séchoir rentables en fonction du risque. Les systèmes où la récolte et le séchage sont terminés au bout de 27 à 32 jours entraînent de légères variations du revenu net, pour les trois types d'exploitation. Peu importe la taille de la ferme, l'aversion pour le risque incite le producteur à choisir une moissonneuse et un séchoir de dimensions plus modestes.

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Optimising harvest operations against weather risk

Abawi

1993

Systems Approaches for Agricultural Development

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Most wheat in Australia is harvested at a maximum moisture content of 12070 wet basis (w.b.). Delaying harvest until the grain is naturally dried to 12% results in yield and quality losses. Losses due to rain on the mature crop cost the grain industry around $A 30 million annually ($A 1 = $US 0.75). These losses are significantly higher in the northern wheat belt of Australia due to a dominant summer rainfall which coincides with the harvest period. If a wet season can be predicted, growers could reduce grain losses by implementing strategies such as early harvesting, contract harvesting, additional grain drying and harvesting at a faster rate.Weather prediction is possible several months ahead by the analysis of sea surface temperature and air pressure differences between Tahiti and Darwin, as identified by the Southern Oscillation Index. Long range weather forecasts could enable farmers to maximise profits by altering their management practices according to seasonal variations in rainfall.This paper examines the interactions between weather, crop, machinery selection and management practices during wheat harvesting in northern Australia. A simulation model was used to investigate the effect of harvesting at high grain moisture content, then drying artificially to reduce field and quality losses. The model predicts that additional returns of up to $A 140 ha-1 are possible by adopting long term strategic harvesting decisions in the northern wheat belt. Returns may be further increased by $A 20 to $A 40 ha-1 through optimising harvest operations during each season. Optimum machinery capacities and harvest strategies for a range of crop areas are outlined.

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Optimal harvest policies for corn and soybeans

Cited by 9 publications

References 5 publications

Orange harvesting scheduling management: a case study

Orange harvesting scheduling management: a case study

The Effect of Producers' Risk Attitudes on Sizing the Harvest and On‐farm Drying System

Optimising harvest operations against weather risk

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