Restricted Estimation of Crop and Summerfallow Acreage Response in Saskatchewan

Clark, Jim; Klein, K. K.

doi:10.1111/j.1744-7976.1992.tb03709.x

Cited by 10 publications

(6 citation statements)

References 11 publications

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“…Both the econometric estimates and expert assessments (e.g., Li et al., 2019; Tabeau et al., 2017; Baldos & Hertel, 2013; Gurgel et al., 2007; Clark & Klein, 1992) of own‐price elasticities of land supply tend to be low, suggesting a value for this parameter somewhere between 0.04 and 0.30. Regarding the non‐land‐owned inputs, which are characterized by their highly technical nature and mostly include specialized fixed capital and human capital inputs, their supply elasticities are often inelastic.…”

Section: Data and Parameter Values In The Edmmentioning

confidence: 99%

The distribution of returns from land efficiency improvement in multistage production systems

Awada

Phillips

2020

Canadian J Agri Economics

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This paper assesses the distributional consequences of technical changes that improve the efficiency of land and of other inputs in a multifactor crop‐production system. We introduced an equilibrium displacement model (EDM) by using the specification of a factor‐augmenting approach. Given the uncertainty about the EDM parameters, a Monte Carlo simulation is used to produce a distribution of possible return measures. We found that land suppliers (likely farmers) receive a larger share (73%) of total benefits from the adoption of land‐technical change than they do from the adoption of other input technologies. Each input supplier receives a larger share of total benefits from technical change in her own input. However, this result is sensitive to the value of the parameters, especially the value of the elasticity of substitution. We applied the EDM to the case of no‐tillage (NT) to provide insight into how the aggregate return from the adoption of NT was distributed among different groups on the Canadian Prairies. The results of this study can be used by policymakers and funding agencies in order to influence landowners and farming communities to adopt environmentally sound land technologies to achieve both greater agricultural productivity and sustainability.

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Section: Data and Parameter Values In The Edmmentioning

confidence: 99%

The distribution of returns from land efficiency improvement in multistage production systems

Awada

Phillips

2020

Canadian J Agri Economics

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“…Recent examples are cited by Clark and Klein (1992) and Coyle (1993). In general, it is too costly and too impractical to observe producer price expectations directly.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, most researchers rely on proxy variables based on past prices to represent producer expectations. Recent examples are cited by Clark and Klein (1992) and Coyle (1993). However, according to Chavas and Johnson (1982), the specification of these relationships is crucial.…”

Section: Introductionmentioning

confidence: 99%

Agricultural Producer Price Expectations

Sulewski

Sprigs

Schoney

1994

Canadian J Agri Economics

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Producer price expectations underlie much of agricultural supply analysis. While producer price expectations would ideally be discovered experimentally, this is too costly. Instead, producer price expectations are usually represented in agricultural supply analysis by easily obtained hypothesized expectation formulations. In most cases, the hypothesized expectation formulations are finctions of past prices. However, other formulations are sometimes used, such as current cash and fitures prices, or initial payments in the case of grains marketed by the Canadian Wheat Board. This paper compares actual producer price expectations with a variety of hypothesized expectation formulations for wheat ana' canola in Saskatchewan. A test developed by Granger is used to determine the proxy models that are signiJicantly dominant. i%e model that dominates as a proxy in the case of wheat price expectations is the two-year declining-weight moving average. The two models that dominate as a proxy in the case of canola price expectations are the first-order autoregressive and, as well, the two-year decliningweight moving average. There is no signi$cant diference between the two models. Somewhat surprising is the pet$ormance of formulations based on futures prices. These formulations perform very poorly in representing producers 'price expectations, even though they are found to be among the most accurate predictors of actual commodity prices. An even more interesting observation is the pe$ormance of the futures price model in the canola market. Even though the November contract in January explains very little of the variation in the actual commodity prices for that year, its error in predicting canola prices is not signtftcantly greater than that of the best performing, the four-year declining-weight moving average, based upon the root mean squared error criterion. Les esperances de prix a la production sous-tendent une bonne partie de l'analyse des foumitures agn'coles. Ces esperances pourraient Ctre identt@es par voie experimentale mais ce serait trop onereux. Plutot, elles sont habituellement representees dans 1 'analyse des fournitures agricoles par des formules facilement accessibles. Dans la plupart des cas, ces formule sont fonction des prix anterieurs. On utilise aussi d'autres for-mules comme les liquidites courantes et les prix a terme ou encore les paiements d 'acompte darts le cas des cereales vendues par 1 'entremise de la Commission canadienne du blk. Nous comparons les esperances reelles de prix a la production avec diverses formules hypothetiques de calcul des esperances relativement au ble et au colza canola en Saskatchewan. Nous avons utilise' le test de Granger pour determiner les modeles de remplacement hypothetiques qui sont signiflcativement dominants. Le modele de remplacement dominant dans le cas des esperances de prti du ble est la moyenne mobile de deux ans a pond&ration regressive. Les deux modeles dominants dans le cas du colza canola sont les esperances autoregressives de premier ordre, ainsi que la moy...

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“…Land's cost share lies between 0.22 and 0.27, making the data-driven service's share 0.10 to 0.21. Land supply elasticity is typically low, between 0.1 and 0.3 (Baldos & Hertel, 2013;Clark & Klein, 1992;Gurgel et al, 2007;Li et al, 2019;Tabeau et al, 2017). Conversely, purchased inputs, being more adaptable, have a higher elasticity between 2 and 5 (Martini, 2011;Zhao et al, 2003).…”

Section: Data and Parameter Valuesmentioning

confidence: 99%

Market power in smart farming and the distribution of gains in two‐stage crop production system

Awada,

Phillips

2023

J of Agr & App Econ Assoc

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In smart farming, agricultural technology providers (ATPs) wield market power in both the upstream (data collection/aggregation) and downstream (crop production) markets. Using a two‐stage Muth model, this study assesses benefit distribution from ATPs' data‐driven services in smart farming. Results show limited farmer returns from data sharing, questioning policymakers' data value focus. While data‐driven services offer notable benefits, ATPs capture a significant share due to market power. Addressing ATP market power promotes equitable rent distribution, but perfect competition risks ATPs' sustainability and R&D incentives, presenting a policy challenge for smart farming outcomes.

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Restricted Estimation of Crop and Summerfallow Acreage Response in Saskatchewan

Cited by 10 publications

References 11 publications

The distribution of returns from land efficiency improvement in multistage production systems

The distribution of returns from land efficiency improvement in multistage production systems

Agricultural Producer Price Expectations

Market power in smart farming and the distribution of gains in two‐stage crop production system

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