Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Abstract

ABSTRACT:Remote sensing (RS) data acquired by satellite have wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near-infrared RS data have been extensively used for crop characterization, biomass estimation and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant-water stress. This paper reports the development of an operational spectro-agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT-VEGETATION, meteorological data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) model and crop-water status indicators estimated by the Crop Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted and production were used in the model. The statistical multiple regression linear model has been developed for six large maize-growing provinces in Kenya. The spectro-agrometerological yield model was validated by comparing the predicted province-level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t/ha. Very encouraging results were obtained when the Jack-knife re-sampling technique was applied proving the validity of the forecast capability of the model (r 2 = 0.81 and RMSE= 0.359 t/ha). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles respectively. The national maize production during the first crop season for the years 1998 to 2003 was estimated with a RMSE of 185 060 tons and coefficient of variation of 9 %.

Length of Growing Period over Africa: Variability and Trends from 30 Years of NDVI Time Series

“…Other options include a cumulative NDVI (or another drought index) over that part of the growing period which is sensitive to droughts [64].…”

Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI

“…It is therefore important to apply a method that will normalize data by removing time lag since this will decrease the effect of mixed crop-natural vegetation pixels in the satellite data used for yield forecasting. The effect of mixed pixels while developing a maize yield model using the land cover weighted NDVI rather than the traditional NDVI reduced the unknown variance by 26% [21]. It was argued that yield estimation using NDVI may vary during respective months of the crop growth because NDVI is reduced at the end of the rainy season, emphasising the need for careful consideration on time integration [11].…”

Desenvolvimento de modelo agrometeorológico espectral para estimativa de rendimento do milho na Província de Manica-Moçambique

“…Foi considerado o decêndio de semeadura, aquele que apresentou registro de precipitação pluvial superior a 25 mm, seguido de dois decêndios com precipitação pluvial total superior a 20 mm, pois sabe-se que, após um período de chuvas, inicia-se o plantio na totalidade das áreas cultivadas pelos agricultores da região. Este critério é comum para a definição do início do ciclo e foi aplicado, por alguns autores, em casos semelhantes, no continente africano, e, especificamente, em Moçambique (Cumba, 2001;Cambaza, 2007;Rojas, 2007;Mabilana, 2008). O modelo de estimativa de início do ciclo foi implementado com base nas estimativas decendiais de precipitação pluvial.…”

“…O RMSE é uma medida de erro total de um dado modelo, definida pela raiz quadrada da soma das variâncias (equação 10). Esta medida assume que o maior erro na estimativa do rendimento tenha maior peso proporcional que os erros menores (Rojas, 2007). (10) na qual Y é o rendimento médio observado, o rendimento médio estimado pela expressão matemática do modelo e o N o número da amostra usada no ajuste.…”

“…O mapeamento realizado, apesar de não possuir validação em campo, é baseado em pressupostos teóricos sólidos. Rojas (2007) ajustou com sucesso o modelo de estimativa de rendimentos com dados de NDVI/SPOT VGT, com resolução de 1 km, em campos fragmentados de milho, no Quênia. A grande vantagem do uso desta metodologia para fins de segurança alimentar reside na facilidade de obtenção de dados para ajuste, que se encontram disponíveis para download na internet, sem qualquer custo monetá-rio e a um processamento elevado (caso concreto das imagens MODIS: correção atmosférica e georeferenciamento), reduzindo o tempo de calibração e,ou, normalização necessárias em análises multitemporais.…”