The Experts below are selected from a list of 837 Experts worldwide ranked by ideXlab platform
Frank Ewert - One of the best experts on this subject based on the ideXlab platform.
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limited potential of Crop management for mitigating surface ozone impacts on global food supply
Atmospheric Environment, 2011Co-Authors: Edmar Teixeira, G Fischer, Harrij Van Velthuizen, Rita Van Dingenen, Frank Dentener, Gina Mills, Christof Walter, Frank EwertAbstract:Abstract Surface ozone (O 3 ) is a potent phytotoxic air pollutant that reduces the productivity of agricultural Crops. Growing use of fossil fuel and climate change are increasing O 3 concentrations to levels that threaten food supply. Historically, farmers have successfully adapted agricultural practices to cope with changing environments. However, high O 3 concentrations are a new threat to food production and possibilities for adaptation are not well understood. We simulate the impact of ozone damage on four key Crops (wheat, maize, rice and soybean) on a global scale and assess the effectiveness of adaptation of agricultural practices to minimize ozone damage. As O 3 concentrations have a strong seasonal and regional pattern, the adaptation options assessed refer to shifting Crop Calendars through changing sowing dates, applying irrigation and using Crop varieties with different growth cycles. Results show that China, India and the United States are currently by far the most affected countries, bearing more than half of all global losses and threatened areas. Irrigation largely affects ozone exposure but local impacts depend on the seasonality of emissions and climate. Shifting Crop Calendars can reduce regional O 3 damage for specific Crop-location combinations (e.g. up to 25% for rain-fed soybean in India) but has little implication at the global level. Considering the limited benefits of adaptation, mitigation of O 3 precursors remains the main option to secure regional and global food production.
Kenji Tanaka - One of the best experts on this subject based on the ideXlab platform.
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sacra a method for the estimation of global high resolution Crop Calendars from a satellite sensed ndvi
Hydrology and Earth System Sciences, 2015Co-Authors: Shunji Kotsuki, Kenji TanakaAbstract:To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A Crop calendar (CC), which defines the date or month when farmers sow and harvest Crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived Crop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six Crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant Crop and estimate CCs of the dominant Crop in each grid. The dominant Crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several Crops in a grid is not considered in SACRA. The assumption of one dominant Crop in each grid is a major source of discrepancy in Crop Calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and Crop-type classification studies to consider several dominant Crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations.
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SACRA – a method for the estimation of global high-resolution Crop Calendars from a satellite-sensed NDVI
Copernicus Publications, 2015Co-Authors: Shunji Kotsuki, Kenji TanakaAbstract:To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A Crop calendar (CC), which defines the date or month when farmers sow and harvest Crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived Crop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six Crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant Crop and estimate CCs of the dominant Crop in each grid. The dominant Crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several Crops in a grid is not considered in SACRA. The assumption of one dominant Crop in each grid is a major source of discrepancy in Crop Calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and Crop-type classification studies to consider several dominant Crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations
Shunji Kotsuki - One of the best experts on this subject based on the ideXlab platform.
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sacra a method for the estimation of global high resolution Crop Calendars from a satellite sensed ndvi
Hydrology and Earth System Sciences, 2015Co-Authors: Shunji Kotsuki, Kenji TanakaAbstract:To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A Crop calendar (CC), which defines the date or month when farmers sow and harvest Crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived Crop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six Crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant Crop and estimate CCs of the dominant Crop in each grid. The dominant Crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several Crops in a grid is not considered in SACRA. The assumption of one dominant Crop in each grid is a major source of discrepancy in Crop Calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and Crop-type classification studies to consider several dominant Crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations.
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SACRA – a method for the estimation of global high-resolution Crop Calendars from a satellite-sensed NDVI
Copernicus Publications, 2015Co-Authors: Shunji Kotsuki, Kenji TanakaAbstract:To date, many studies have performed numerical estimations of biomass production and agricultural water demand to understand the present and future supply–demand relationship. A Crop calendar (CC), which defines the date or month when farmers sow and harvest Crops, is an essential input for the numerical estimations. This study aims to present a new global data set, the SAtellite-derived Crop calendar for Agricultural simulations (SACRA), and to discuss advantages and disadvantages compared to existing census-based and model-derived products. We estimate global CC at a spatial resolution of 5 arcmin using satellite-sensed normalized difference vegetation index (NDVI) data, which corresponds to vegetation vitality and senescence on the land surface. Using the time series of the NDVI averaged from three consecutive years (2004–2006), sowing/harvesting dates are estimated for six Crops (temperate-wheat, snow-wheat, maize, rice, soybean and cotton). We assume time series of the NDVI represent the phenology of one dominant Crop and estimate CCs of the dominant Crop in each grid. The dominant Crops are determined using harvested areas based on census-based data. The cultivation period of SACRA is identified from the time series of the NDVI; therefore, SACRA considers current effects of human decisions and natural disasters. The difference between the estimated sowing dates and other existing products is less than 2 months (< 62 days) in most of the areas. A major disadvantage of our method is that the mixture of several Crops in a grid is not considered in SACRA. The assumption of one dominant Crop in each grid is a major source of discrepancy in Crop Calendars between SACRA and other products. The disadvantages of our approach may be reduced with future improvements based on finer satellite sensors and Crop-type classification studies to consider several dominant Crops in each grid. The comparison of the CC also demonstrates that identification of wheat type (sowing in spring or fall) is a major source of error in global CC estimations
D.m. Kadiyala - One of the best experts on this subject based on the ideXlab platform.
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Assessing climate risks in rainfed farming using farmer experience, Crop Calendars and climate analysis
The Journal of Agricultural Science, 2015Co-Authors: U. B. Nidumolu, P. T. Hayman, Z. Hochman, H. Horan, D. R. Reddy, G. Sreenivas, D.m. KadiyalaAbstract:SUMMARYClimate risk assessment in Cropping is generally undertaken in a top-down approach using climate records while critical farmer experience is often not accounted for. In the present study, set in south India, farmer experience of climate risk is integrated in a bottom-up participatory approach with climate data analysis. Crop Calendars are used as a boundary object to identify and rank climate and weather risks faced by smallhold farmers. A semi-structured survey was conducted with experienced farmers whose income is predominantly from farming. Interviews were based on a Crop calendar to indicate the timing of key weather and climate risks. The simple definition of risk as consequence × likelihood was used to establish the impact on yield as consequence and chance of occurrence in a 10-year period as likelihood. Farmers’ risk experience matches well with climate records and risk analysis. Farmers’ rankings of ‘good’ and ‘poor’ seasons also matched up well with their independently reported yield data. On average, a ‘good’ season yield was 1·5–1·65 times higher than a ‘poor’ season. The main risks for paddy rice were excess rains at harvesting and flowering and deficit rains at transplanting. For cotton, farmers identified excess rain at harvest, delayed rains at sowing and excess rain at flowering stages as events that impacted Crop yield and quality. The risk assessment elicited from farmers complements climate analysis and provides some indication of thresholds for studies on climate change and seasonal forecasts. The methods and analysis presented in the present study provide an experiential bottom-up perspective and a methodology on farming in a risky rainfed climate. The methods developed in the present study provide a model for end-user engagement by meteorological agencies that strive to better target their climate information delivery.
Russell F Reinke - One of the best experts on this subject based on the ideXlab platform.
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riceatlas a spatial database of global rice Calendars and production
Scientific Data, 2017Co-Authors: Alice G Laborte, Mary Anne Gutierrez, Jane Girly Balanza, Kazuki Saito, Sander J Zwart, Mirco Boschetti, M V R Murty, Lorena Villano, Jorrel Khalil Aunario, Russell F ReinkeAbstract:Knowing where, when, and how much rice is planted and harvested is crucial information for understanding the effects of policy, trade, and global and technological change on food security. We developed RiceAtlas, a spatial database on the seasonal distribution of the world's rice production. It consists of data on rice planting and harvesting dates by growing season and estimates of monthly production for all rice-producing countries. Sources used for planting and harvesting dates include global and regional databases, national publications, online reports, and expert knowledge. Monthly production data were estimated based on annual or seasonal production statistics, and planting and harvesting dates. RiceAtlas has 2,725 spatial units. Compared with available global Crop Calendars, RiceAtlas is nearly ten times more spatially detailed and has nearly seven times more spatial units, with at least two seasons of calendar data, making RiceAtlas the most comprehensive and detailed spatial database on rice calendar and production.