Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data

Reynolds, Curt; Yitayew, Muluneh; Slack, D. C.; Hutchinson, C.; Huete, A.; Petersen, Margaret S.

doi:10.1080/014311600750037516

Cited by 171 publications

(105 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…K y values for individual periods and the complete growing period are given in Doorenbos and Kassam (1979). The maximum yield values for each crop were obtained by multiplying the corresponding national average yield values by a factor of 1.2 (Reynolds et al, 2000). The actual yields, which are calculated per grid cell, are averaged over the nation and compared with the national average yield data (for the period 1996-2005) obtained from FAO (2008a).…”

Section: Methods and Datamentioning

confidence: 99%

The green, blue and grey water footprint of crops and derived crop products

Mekonnen

Hoekstra

2011

Hydrol. Earth Syst. Sci.

1,827

1,492

View full text Add to dashboard Cite

Abstract. This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network.Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m 3 ton −1 ), vegetables (300 m 3 ton −1 ), roots and tubers (400 m 3 ton −1 ), fruits (1000 m 3 ton −1 ), cereals (1600 m 3 ton −1 ), oil crops (2400 m 3 ton −1 ) to pulses (4000 m 3 ton −1 ). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m 3 GJ −1 ) than biodiesel, which supports earlier analyses. The crop used matters significantly The global water footprint related to crop production in the period 1996-2005 was 7404 billion cubic meters per year (78 % green, 12 % blue, 10 % grey). A large total water footprint was calculated for wheat (1087 Gm 3 yr −1 ), rice (992 Gm 3 yr −1 ) and maize (770 Gm 3 yr −1 ). Wheat and rice have the largest blue water footprints, together accounting for 45% of the global blue water footprint. At country level, the total water footprint was largest for India (1047 Gm 3 yr −1 ), China (967 Gm 3 yr −1 ) and the USA (826 Gm 3 yr −1 ). A relatively large total blue water footprint as a result of crop production is observed in the Indus river basin (117 Gm 3 yr −1 ) and the Ganges river basin (108 Gm 3 yr −1 ). The two basins together account for 25% of the blue water footprint related to global crop production. Globally, rain-fed agriculture has a water footprint of 5173 Gm 3 yr −1 (91% green, 9 % grey); irrigated agriculture has a water footprint of 2230 Gm 3 yr −1 (48 % green, 40 % blue, 12 % grey).

show abstract

Section: Methods and Datamentioning

confidence: 99%

The green, blue and grey water footprint of crops and derived crop products

Mekonnen

Hoekstra

2011

Hydrol. Earth Syst. Sci.

1,827

1,492

View full text Add to dashboard Cite

show abstract

“…The crop production assessments are obtained by taking products of crop acreage estimates and corresponding crop yield estimates. Such techniques are often subjective, costly and are prone to large errors, leading to poor crop assessment and crop area estimation (Reynolds et al, 2000). Also, the collected data may become available too late for appropriate action to be taken to ward off food shortage.…”

Section: Issn: 2319-7706 Volume 6 Number 12 (2017) Pp 2277-2293mentioning

confidence: 99%

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Ajith¹,

Geethalakshmi²,

Ragunath³

et al. 2017

Int.J.Curr.Microbiol.App.Sci

View full text Add to dashboard Cite

“…In terms of yield/production information, the BBS collects data from 9,348 number of plots across the country (BBS, 2010). Despite the accuracy of these data and its ability to depict historical trends, this method has two major drawbacks: (i) time-consuming, subjective, costly, and labour-intensive (Reynolds et al, 2000;Prasad et al, 2006;Nguyen et al, 2012); and (ii) the outcomes are usually made available to the government and public after several months of the harvesting of the crop; thus not useful for food security purposes (Noureldin et al, 2013). In order to address these issues, an alternate method is the use of the remote sensing-based techniques that have already demonstrated effectiveness in forecasting the rice yield (Jing-Feng et al, 2002;Wang et al, 2010;Chen et al, 2011;Nuarsa et al, 2012;Huang et al, 2013) and assessing the yield for other crops (Bonilla et al, 2015;Fortes et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Development of a remote sensing-based rice yield forecasting model

Mosleh

Hassan

Chowdhury

2016

Span. j. agric. res.

View full text Add to dashboard Cite

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

show abstract

Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data

Cited by 171 publications

References 0 publications

The green, blue and grey water footprint of crops and derived crop products

The green, blue and grey water footprint of crops and derived crop products

Rice Yield Prediction Using MODIS - NDVI (MOD13Q1) and Land Based Observations

Development of a remote sensing-based rice yield forecasting model

Contact Info

Product

Resources

About