The Experts below are selected from a list of 237 Experts worldwide ranked by ideXlab platform

Martin K Van Ittersum - One of the best experts on this subject based on the ideXlab platform.

  • The Crop yield gap between organic and conventional agriculture
    Agricultural Systems, 2012
    Co-Authors: Tomek De Ponti, Bert Rijk, Martin K Van Ittersum
    Abstract:

    A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic-conventional comparative Crop yields. Our results show that organic yields of individual Crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between Crop Groups and regions. The analysis gave some support to our hypothesis that the organic-conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture.We discuss our findings in the context of the literature on this subject and address the issue of upscaling our results to higher system levels. Our analysis was at field and Crop level. We hypothesize that due to challenges in the maintenance of nutrient availability in organic systems at Crop rotation, farm and regional level, the average yield gap between conventional and organic systems may be larger than 20% at higher system levels. This relates in particular to the role of legumes in the rotation and the farming system, and to the availability of (organic) manure at the farm and regional levels. Future research should therefore focus on assessing the relative performance of both types of agriculture at higher system levels, i.e. the farm, regional and global system levels, and should in that context pay particular attention to nutrient availability in both organic and conventional agriculture. © 2012 Elsevier Ltd.

Z Hill - One of the best experts on this subject based on the ideXlab platform.

  • Spectral classification of Crop Groups for land use identification with temporally sparse time-series satellite images
    2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
    Co-Authors: H. C. North, D. Pairman, S. E. Belliss, S. J. Mcneill, J. Cuff, Z Hill
    Abstract:

    In previous work we demonstrated the use of temporal image sequences to identify broad land use classes [1]. The approach aims to provide information critical to modeling land use impacts while minimizing reliance on collecting ground control for individual images. Here, we extend the method to include spectral information taken at the peak NDVI stage for each field. Results show the level of spectral separability of various key Crops and pastures, and how we have grouped certain Crops that are not spectrally separable. Whereas we obtained only 42% classification accuracy when attempting to classify Crops individually, the classification accuracy for our Crop Groups was 81%. A major challenge is that image datasets are typically sparse - due to cloud cover in New Zealand - so the growth stage, and therefore appearance, of individual Crops can vary widely in the `peak' NDVI image.

  • IGARSS - Spectral classification of Crop Groups for land use identification with temporally sparse time-series satellite images
    2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
    Co-Authors: Heather North, D. Pairman, S. E. Belliss, S. J. Mcneill, J. Cuff, Z Hill
    Abstract:

    In previous work we demonstrated the use of temporal image sequences to identify broad land use classes [1]. The approach aims to provide information critical to modeling land use impacts while minimizing reliance on collecting ground control for individual images. Here, we extend the method to include spectral information taken at the peak NDVI stage for each field. Results show the level of spectral separability of various key Crops and pastures, and how we have grouped certain Crops that are not spectrally separable. Whereas we obtained only 42% classification accuracy when attempting to classify Crops individually, the classification accuracy for our Crop Groups was 81%. A major challenge is that image datasets are typically sparse - due to cloud cover in New Zealand - so the growth stage, and therefore appearance, of individual Crops can vary widely in the `peak' NDVI image.

Tomek De Ponti - One of the best experts on this subject based on the ideXlab platform.

  • The Crop yield gap between organic and conventional agriculture
    Agricultural Systems, 2012
    Co-Authors: Tomek De Ponti, Bert Rijk, Martin K. Van Ittersum
    Abstract:

    A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic–conventional comparative Crop yields. Our results show that organic yields of individual Crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between Crop Groups and regions. The analysis gave some support to our hypothesis that the organic–conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture.

  • The Crop yield gap between organic and conventional agriculture
    Agricultural Systems, 2012
    Co-Authors: Tomek De Ponti, Bert Rijk, Martin K Van Ittersum
    Abstract:

    A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic-conventional comparative Crop yields. Our results show that organic yields of individual Crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between Crop Groups and regions. The analysis gave some support to our hypothesis that the organic-conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture.We discuss our findings in the context of the literature on this subject and address the issue of upscaling our results to higher system levels. Our analysis was at field and Crop level. We hypothesize that due to challenges in the maintenance of nutrient availability in organic systems at Crop rotation, farm and regional level, the average yield gap between conventional and organic systems may be larger than 20% at higher system levels. This relates in particular to the role of legumes in the rotation and the farming system, and to the availability of (organic) manure at the farm and regional levels. Future research should therefore focus on assessing the relative performance of both types of agriculture at higher system levels, i.e. the farm, regional and global system levels, and should in that context pay particular attention to nutrient availability in both organic and conventional agriculture. © 2012 Elsevier Ltd.

Bert Rijk - One of the best experts on this subject based on the ideXlab platform.

  • The Crop yield gap between organic and conventional agriculture
    Agricultural Systems, 2012
    Co-Authors: Tomek De Ponti, Bert Rijk, Martin K. Van Ittersum
    Abstract:

    A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic–conventional comparative Crop yields. Our results show that organic yields of individual Crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between Crop Groups and regions. The analysis gave some support to our hypothesis that the organic–conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture.

  • The Crop yield gap between organic and conventional agriculture
    Agricultural Systems, 2012
    Co-Authors: Tomek De Ponti, Bert Rijk, Martin K Van Ittersum
    Abstract:

    A key issue in the debate on the contribution of organic agriculture to the future of world agriculture is whether organic agriculture can produce sufficient food to feed the world. Comparisons of organic and conventional yields play a central role in this debate. We therefore compiled and analyzed a meta-dataset of 362 published organic-conventional comparative Crop yields. Our results show that organic yields of individual Crops are on average 80% of conventional yields, but variation is substantial (standard deviation 21%). In our dataset, the organic yield gap significantly differed between Crop Groups and regions. The analysis gave some support to our hypothesis that the organic-conventional yield gap increases as conventional yields increase, but this relationship was only rather weak. The rationale behind this hypothesis is that when conventional yields are high and relatively close to the potential or water-limited level, nutrient stress must, as per definition of the potential or water-limited yield levels, be low and pests and diseases well controlled, which are conditions more difficult to attain in organic agriculture.We discuss our findings in the context of the literature on this subject and address the issue of upscaling our results to higher system levels. Our analysis was at field and Crop level. We hypothesize that due to challenges in the maintenance of nutrient availability in organic systems at Crop rotation, farm and regional level, the average yield gap between conventional and organic systems may be larger than 20% at higher system levels. This relates in particular to the role of legumes in the rotation and the farming system, and to the availability of (organic) manure at the farm and regional levels. Future research should therefore focus on assessing the relative performance of both types of agriculture at higher system levels, i.e. the farm, regional and global system levels, and should in that context pay particular attention to nutrient availability in both organic and conventional agriculture. © 2012 Elsevier Ltd.

D. Pairman - One of the best experts on this subject based on the ideXlab platform.

  • Spectral classification of Crop Groups for land use identification with temporally sparse time-series satellite images
    2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
    Co-Authors: H. C. North, D. Pairman, S. E. Belliss, S. J. Mcneill, J. Cuff, Z Hill
    Abstract:

    In previous work we demonstrated the use of temporal image sequences to identify broad land use classes [1]. The approach aims to provide information critical to modeling land use impacts while minimizing reliance on collecting ground control for individual images. Here, we extend the method to include spectral information taken at the peak NDVI stage for each field. Results show the level of spectral separability of various key Crops and pastures, and how we have grouped certain Crops that are not spectrally separable. Whereas we obtained only 42% classification accuracy when attempting to classify Crops individually, the classification accuracy for our Crop Groups was 81%. A major challenge is that image datasets are typically sparse - due to cloud cover in New Zealand - so the growth stage, and therefore appearance, of individual Crops can vary widely in the `peak' NDVI image.

  • IGARSS - Spectral classification of Crop Groups for land use identification with temporally sparse time-series satellite images
    2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS, 2013
    Co-Authors: Heather North, D. Pairman, S. E. Belliss, S. J. Mcneill, J. Cuff, Z Hill
    Abstract:

    In previous work we demonstrated the use of temporal image sequences to identify broad land use classes [1]. The approach aims to provide information critical to modeling land use impacts while minimizing reliance on collecting ground control for individual images. Here, we extend the method to include spectral information taken at the peak NDVI stage for each field. Results show the level of spectral separability of various key Crops and pastures, and how we have grouped certain Crops that are not spectrally separable. Whereas we obtained only 42% classification accuracy when attempting to classify Crops individually, the classification accuracy for our Crop Groups was 81%. A major challenge is that image datasets are typically sparse - due to cloud cover in New Zealand - so the growth stage, and therefore appearance, of individual Crops can vary widely in the `peak' NDVI image.