Use of agro-climatic zones to upscale simulated crop yield potential

Wart, Justin Van; Bussel, L.G.J. van; Wolf, J.; Licker, Rachel; Grassini, Patricio; Nelson, Andrew; Boogaard, H.L.; Gerber, James; Mueller, Nathaniel D.; Claessens, Lieven; Ittersum, M.K. van; Cassman, Kenneth G.

doi:10.1016/j.fcr.2012.11.023

Cited by 275 publications

(204 citation statements)

References 37 publications

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“…We included all of the climate data that was processed in the form of bioclimatic variables [53,54]. These climate variables can be considered drivers of LULC change because the agro-climatic zones with different climatic conditions can affect the suitability of agricultural lands for its productivity [55]; thus, no patterns of change in the preferred area might be identified relative to this factor. This is one challenge in our work since not many related studies implement climatic information in their calibration.…”

Section: Collection and Processing Of Data On Potential Driver Variablesmentioning

confidence: 99%

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Iizuka

Johnson

Onishi

et al. 2017

Land

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This study uses a spatially-explicit land-use/land-cover (LULC) modeling approach to model and map the future (2016-2030) LULC of the area surrounding the Laguna de Bay of Philippines under three different scenarios: 'business-as-usual', 'compact development', and 'high sprawl' scenarios. The Laguna de Bay is the largest lake in the Philippines and an important natural resource for the population in/around Metro Manila. The LULC around the lake is rapidly changing due to urban sprawl, so local and national government agencies situated in the area need an understanding of the future (likely) LULC changes and their associated hydrological impacts. The spatial modeling approach involved three main steps: (1) mapping the locations of past LULC changes; (2) identifying the drivers of these past changes; and (3) identifying where and when future LULC changes are likely to occur. Utilizing various publically-available spatial datasets representing potential drivers of LULC changes, a LULC change model was calibrated using the Multilayer Perceptron (MLP) neural network algorithm. After calibrating the model, future LULC changes were modeled and mapped up to the year 2030. Our modeling results showed that the 'built-up' LULC class is likely to experience the greatest increase in land area due to losses in 'crop/grass' (and to a lesser degree 'tree') LULC, and this is attributed to continued urban sprawl.

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Section: Collection and Processing Of Data On Potential Driver Variablesmentioning

confidence: 99%

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Iizuka

Johnson

Onishi

et al. 2017

Land

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“…AEZ-based stratification methods were used to divide the study area into more homogeneous regions in previous research such as simulating crop yield potentials [42], global change assessment the [43] and climate change [44] projects. Therefore, AEZ-based stratification method will also help to divide different continents into homogeneous regions to implement the assessment strategy required to assess the cropland extent maps [45].…”

Section: Stratificationmentioning

confidence: 99%

Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents

Yadav

Congalton

2017

Remote Sensing

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Accurate, consistent and timely cropland information over large areas is critical to solve food security issues. To predict and respond to food insecurity, global cropland products are readily available from coarse and medium spatial resolution earth observation data. However, while the use of satellite imagery has great potential to identify cropland areas and their specific types, the full potential of this imagery has yet to be realized due to variability of croplands in different regions. Despite recent calls for statistically robust and transparent accuracy assessment, more attention regarding the accuracy assessment of large area cropland maps is still needed. To conduct a valid assessment of cropland maps, different strategies, issues and constraints need to be addressed depending upon various conditions present in each continent. This study specifically focused on dealing with some specific issues encountered when assessing the cropland extent of North America (confined to the United States), Africa and Australia. The process of accuracy assessment was performed using a simple random sampling design employed within defined strata (i.e., Agro-Ecological Zones (AEZ's) for the US and Africa and a buffer zone approach around the cropland areas of Australia. Continent-specific sample analysis was performed to ensure that an appropriate reference data set was used to generate a valid error matrix indicative of the actual cropland proportion. Each accuracy assessment was performed within the homogenous regions (i.e., strata) of different continents using different sources of reference data to produce rigorous and valid accuracy results. The results indicate that continent-specific modified assessments performed for the three selected continents demonstrate that the accuracy assessment can be easily accomplished for a large area such as the US that has extensive availability of reference data while more modifications were needed in the sampling design for other continents that had little to no reference data and other constraints. Each continent provided its own unique challenges and opportunities. Therefore, this paper describes a tale of these three continents providing recommendations to adapt accuracy assessment strategies and methodologies for validating global cropland extent maps.

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“…Moreover the study by van Wart et al (2013) underscored that GCMs climate change projections provide poor simulation of crop yield.…”

Section: Introductionmentioning

confidence: 99%

Assessment of the Impacts of Climate Change on Maize Production in the Southern and Western Highlands Sub-agro Ecological Zones of Tanzania

Luhunga

2017

Front. Environ. Sci.

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Use of agro-climatic zones to upscale simulated crop yield potential

Cited by 275 publications

References 37 publications

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Modeling Future Urban Sprawl and Landscape Change in the Laguna de Bay Area, Philippines

Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents

Assessment of the Impacts of Climate Change on Maize Production in the Southern and Western Highlands Sub-agro Ecological Zones of Tanzania

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