Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Dempewolf, Jan; Adusei, Bernard; Becker‐Reshef, Inbal; Hansen, Matthew C.; Potapov, Peter; Khan, Anwar; Barker, B.

doi:10.3390/rs6109653

Cited by 103 publications

(70 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The best results for 2010 and 2011 in terms of RMSE value several weeks prior to harvest were 0.3 and 0.5 t ha , respectively (Kogan et al 2013;Kussul et al 2013). In Punjab, Pakistan, the linear regression of peak-season MODIS-derived vegetation indices were used, and the forecasted yields between 2008 and 2013 were within 11.5% of final reported values (Dempewolf et al 2014). …”

Section: Introductionmentioning

confidence: 92%

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Bognár

Kern

Pásztor

et al. 2017

International Journal of Remote Sensing

View full text Add to dashboard Cite

Wheat is one of the most important crops in Hungary, which represents approximately 20% of the entire agricultural area of the country, and about 40% of cereals. A robust yield method has been improved for estimating and forecasting wheat yield in Hungary in the period of 2003-2015 using normalized difference vegetation index (NDVI) derived from the data of the Moderate Resolution Imaging Spectroradiometer. Estimation was made at the end of June -it is generally the beginning of harvest of winter wheat in Hungary -while the forecasts were performed 1-7 weeks earlier. General yield unified robust reference index (GYURRI) vegetation index was calculated each year using different curve-fitting methods to the NDVI time series. The correlation between GYURRI and country level yield data gave correlation coefficient (r) of 0.985 for the examined 13 years in the case of estimation. Simulating a quasi-operative yield estimation process, 10 years ' (2006-2015) yield data was estimated. The differences between the estimated and actual yield data provided by the Hungarian Central Statistical Office were less than 5%, the average difference was 2.5%. In the case of forecasting, these average differences calculated approximately 2 and 4 weeks before the beginning of harvest season were 4.5% and 6.8%, respectively. We also tested the yield estimation procedure for smaller areas, for the 19 counties (Nomenclature of Territorial Units for Statistics-3 level) of Hungary. We found that, the relationship between GYURRI and the county level yield data had r of 0.894 for the years 2003-2014, and by simulating the quasi-operative forecast for 2015, the resulting 19 county average yield values differed from the actual yield as much as 8.7% in average.

show abstract

Section: Introductionmentioning

confidence: 92%

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Bognár

Kern

Pásztor

et al. 2017

International Journal of Remote Sensing

View full text Add to dashboard Cite

show abstract

“…25 We recently reported marked differences in in vitro salbutamol delivery between two VHCs (Babyhaler, BH and Optichamber Diamond, OD) using numerous pediatric breathing patterns. 13 Alarmingly, the drug delivery capacity of BH, which is available in the market globally, was close to zero when measured with obstructive breathing patterns. There seems to be a risk for a very poor or unreliable drug delivery performance with some VHC models, especially in small children with bronchoconstriction (who have a very low V T and high RR).…”

mentioning

confidence: 98%

“…Certain combinations of pMDI-VHC may result in drastic differences in dose output. 4,5,[9][10][11][12][13] Studies evaluating drug delivery by VHCs have mainly been performed with inspiratory flow rates of 20 to 60 L/minute, and only a few have used rates below 10 L/minute. [14][15][16] Nevertheless, drug delivery is dependent on the patient population since the breathing pattern, V T , peak inspiratory flow rate (PIFR), and resting RR all change with growth and development.…”

mentioning

confidence: 99%

“…[14][15][16] Nevertheless, drug delivery is dependent on the patient population since the breathing pattern, V T , peak inspiratory flow rate (PIFR), and resting RR all change with growth and development. 13,[17][18][19] Tidal volume is approximately 7 to 10 mL/kg of ideal body weight 19 in the first few years of life. With age, RR decreases while V T increases.…”

mentioning

confidence: 99%

See 1 more Smart Citation

In vitro drug delivery performance of five valved holding chambers with and without facemasks

Csonka

Lehtimäki

2019

Pediatric Pulmonology

View full text Add to dashboard Cite

Background Valved holding chambers (VHCs) are essential for efficient pulmonary delivery of inhaled medication in preschool children. The numerous devices in the market vary in material, aerodynamic characteristics, volume, valve properties, and mask design. Drug delivery is affected by the VHC characteristics as well as by the age and breathing pattern of the child. Methods We measured the drug delivery efficacy of five VHCs widely available in the market, evaluated the effect of facemasks and tested the differences between manufacturing lots. A breathing simulator was used to mimic normal (respiratory rate [RR] 25/minute and tidal volume (VT) 200 mL) and obstructive (RR 50/minute and VT 50 mL) breathing of infants and toddlers. Results Salbutamol output was significantly higher with a normal breathing pattern compared to the obstructive breathing pattern in most VHCs. Without masks, the differences in the median in vitro filter doses of salbutamol were mainly from 2 to 10‐fold among different types of VHCs. With masks, there was a greater than 20‐fold difference in drug delivery capacity between the most and least effective devices. Most VHCs had a notable variation of performance between individual devices from different lots within the same brand. Conclusions There was an extreme variation in the salbutamol delivery performance among different types of VHCs for both normal and obstructive pediatric breathing patterns with and without masks. This magnitude of performance variability can have significant and unpredictable clinical implications.

show abstract

“…Es decir, se seleccionan una serie de variables (como por ejemplo radiación interceptada por la vegetación, temperatura, humedad etc. ), que en principio pueden o no afectar a la producción, y mediante la aplicación de modelos de regresión se examina si existe o no relación entre esas variables y la producción del cultivo, como ocurre en el caso de los trabajos de Johnson et al (2016) o Dempewolf et al (2014). Entre los modelos empíricos destacan los que usan como variables independientes datos basados en tecnologías de observación de la tierra.…”

Section: Introductionunclassified

Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

et al. 2018

View full text Add to dashboard Cite

Resumen: Existe una relación entre la producción primaria neta del trigo y los índices de vegetación obtenidos de imágenes de satélite. Con frecuencia se utiliza el NDVI (Normalized Difference Vegetation Index) para la estimación de producción y rendimiento de trigo y otros cultivos. Sin embargo, hay pocas investigaciones que utilicen el índice MTCI (MERIS Terrestrial Chlorophyll Index) para conocer el rendimiento y la producción de los cultivos a una escala regional posiblemente debido a la falta de continuidad del sensor MERIS. No obstante, la posibilidad del cálculo de MTCI a partir de Sentinel 2 abre nuevas oportunidades para su aplicación e investigación. En esta investigación se han generado dos modelos empíricos de estimación de producción y rendimiento de trigo en Andalucía. Para ello, se ha empleado la serie temporal completa (imágenes semanales de 2006 a 2011) del índice de vegetación MTCI del sensor satelital MERIS (Medium Resolution Imaging Spectrometer) asociada a los datos de producción y rendimiento del Anuario de estadísticas agrarias y pesqueras de Andalucía (AEAP). Para la creación de estos modelos ha sido necesaria la identificación del periodo óptimo del desarrollo de la planta, la agregación temporal de los valores MTCI usando ese momento óptimo como referencia, relacionar ese índice con observaciones directas de producción y rendimiento a través de agregaciones espaciales mediante la utilización de coberturas SIGPAC y las solicitudes de ayudas PAC, caracterizar la variación del índice en función del año de cultivo y relacionarlo con los datos estadísticos. Los resultados obtenidos indican una correlación estadísticamente significativa (p-valor < 0,05) entre el índice MTCI y los datos de producción y rendimiento recogidos por AEAP (R 2 =0,81 y 0,57, respectivamente).Palabras clave: teledetección, MTCI, modelo, trigo, cosecha, series temporales. Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time seriesAbstract: There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006-2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP-Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the ...

show abstract

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Cited by 103 publications

References 38 publications

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

Yield estimation and forecasting for winter wheat in Hungary using time series of MODIS data

In vitro drug delivery performance of five valved holding chambers with and without facemasks

Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

Contact Info

Product

Resources

About