A spatial high‐resolution model of the dynamics of agricultural land use

Flores, Luis Ambrosio; Martínez, Luis Iglesias; Ferrer, Carmen Marín; Gallego, Valero Pascual; Bermejo, Arturo Serrano

doi:10.1111/j.1574-0862.2008.00296.x

Cited by 5 publications

(3 citation statements)

References 55 publications

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“…This varies from rigid optimisation or equilibrium 60,65,66,68,73,74 to heterogeneous, partial, or time-dependent satisfaction of economic objectives. 39,49,57,59,63,64,67,70,77,80,81,83,85,90,102 A (substantial) minority of models prioritize social or environmental factors, sometimes on the basis of empirical evidence. 37,53,71,74,75,82 As a result, the potential of many models to capture behavioral effects on adaptive capacity appears to be largely unrealised.…”

Section: Type Of Decision-makingmentioning

confidence: 99%

“…Furthermore, despite the explicitly behavioral focus of models included in this review, the majority assume some form of economic rationality as the basis for land management decisions. This varies from rigid optimisation or equilibrium 60,65,66,68,73,74 to heterogeneous, partial, or time‐dependent satisfaction of economic objectives 39,49,57,59,63,64,67,70,77,80,81,83,85,90,102 . A (substantial) minority of models prioritize social or environmental factors, sometimes on the basis of empirical evidence 37,53,71,74,75,82 .…”

Section: Review Findingsmentioning

confidence: 99%

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Behavioral models of climate change adaptation and mitigation in land‐based sectors

Brown

Alexander

Holzhauer

et al. 2017

WIREs Climate Change

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Models of the land system are essential to our understanding of the magnitude and impacts of climate change. These models are required to represent a large number of processes in different sectors, but face particular challenges in describing the individual and social behaviors that underpin climate change mitigation and adaptation. We assess descriptions of these behaviors in existing models, their commonalities and differences, and the uses to which they have been put. We find that behavioral models have a distinct and important role to play in climate research, but that they currently suffer from being strongly sectoral in nature, with agricultural models being the most common and behaviorally rich. There are also clear convergences, with economic‐based decision‐making remaining dominant and behaviors such as diffusion, interaction, anticipation, or learning remaining relatively neglected. Active climate change is also rarely modeled, with adaptation and mitigation generally represented as responses to economic drivers under static climatic conditions. Furthermore, dynamic behaviors, objectives, or decision‐making processes are almost entirely absent, despite their clear relevance to climate change responses. We conclude that models have been more successful in the identification of important processes than in their implementation and that, while some behavioral processes may remain impossible to model, behavioral models of adaptation and mitigation in land‐based sectors have substantial unexplored potential. We suggest that greater attention be paid to the cumulative coverage of models in this field, and that improvements in the representation of certain key behaviors be prioritized. WIREs Clim Change 2017, 8:e448. doi: 10.1002/wcc.448 This article is categorized under: Assessing Impacts of Climate Change > Representing Uncertainty

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Section: Type Of Decision-makingmentioning

confidence: 99%

Section: Review Findingsmentioning

confidence: 99%

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Brown

Alexander

Holzhauer

et al. 2017

WIREs Climate Change

View full text Add to dashboard Cite

show abstract

“…In order to prevent any undesired environmental impact, it is especially relevant to forecast individual decisions made by farmers and to assess their environmental impact. As shown by Ambrosio et al (2008), a crop rotation model is a basis to specify econometric spatial models as a tool to monitor and control the use of natural resources (soil, water and air) by agriculture as well as synthetic waste products used in agriculture, such as pesticides and chemical fertilisers.…”

Section: Other Applications Of Crop Rotation Modelsmentioning

confidence: 99%

Agricultural and environmental information systems: the integrating role of area samples

Ambrósio

Marín

Iglesias

et al. 2009

Span. j. agric. res.

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This article aims to be a contribution to the improvement of agricultural and environmental official statistics. Methods are applied to integrate information from state agency registers regarding crop area with ground data observed in random area samples. To improve the precision of crop area estimates in small areas (municipalities), methods using ground survey and remote sensing are applied. To improve temporal resolution of crop area estimators, methods based on time series analysis are applied. Agro-meteorological models are applied to improve crop yields statistics. A method is shown whereby crop rotation models may be a useful tool to forecast changes in the dynamics of the use of natural resources (soil, water and air) by agriculture and to foresee their environmental impact. Finally, a method to update and disaggregate information from territorial censuses on land uses is applied. These methods and models are illustrated in the framework of an information system belonging to the Spanish Ministry of the Environment and Rural and Marine Affairs. The relative improvement offered by each method is assessed by evaluating the precision gain of the proposed crop area estimates versus those currently used by the aforementioned information system. Additional key words: agrometeorological models, crop rotation models, sampling in time, small area estimation, spatiotemporal econometric models, updating regional censuses and disaggregation of information. Resumen Sistemas de información agraria y medioambiental: el papel integrador de las muestras de áreasEste artículo pretende ser una contribución a la mejora de las estadísticas oficiales agrarias y medioambientales. Se aplican métodos para mejorar la precisión de las estimaciones anuales de la superficie de cultivos, integrando la información relativa a la superficie de cultivos registrada en los registros administrativos con los datos de campo observados en una muestra aleatoria de áreas. Para mejorar la precisión de las estimaciones de la superficie de cultivos en áreas pequeñas (municipios), se aplican métodos que integran datos de teledetección con datos de campo observados en muestras de áreas. Para mejorar la resolución temporal de los estimadores de la superficie de cultivos se aplican métodos de análisis de series temporales a los datos observados en muestras de áreas. Para mejorar las estadísti-cas sobre el rendimiento de las cosechas se aplican modelos agrometeorológicos integrados en muestras de áreas. Se muestra cómo los modelos de rotación de cultivos basados en muestras de áreas pueden ser una herramienta útil para anticiparse a cambios en la dinámica del uso de los recursos naturales (suelo, agua y aire) por parte de la agricultura y prever su impacto medioambiental. Finalmente, se aplica un método para actualizar y desagregar la información de censos territoriales sobre los usos del suelo, a partir de modelos de rotación de cultivos y de muestras de áreas. Estos métodos y modelos se ilustran en el marco de un sistema de información pertenec...

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Comparative Assessment of the Changing Pattern of Land cover along the Southwestern Coast of Nigeria using GIS and Remote Sensing techniques

et al. 2022

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A spatial high‐resolution model of the dynamics of agricultural land use

Cited by 5 publications

References 55 publications

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Behavioral models of climate change adaptation and mitigation in land‐based sectors

Agricultural and environmental information systems: the integrating role of area samples

Comparative Assessment of the Changing Pattern of Land cover along the Southwestern Coast of Nigeria using GIS and Remote Sensing techniques

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