Prediction of crop yield, water consumption and water use efficiency with a SVAT-crop growth model using remotely sensed data on the North China Plain

Zhao

New Zealand Journal of Crop and Horticultural Science

et al. 2010

Quantifying diurnal patterns of water use efficiency (WUE) and radiation use efficiency (RUE) for wheat and maize is important for assessing water use by plants and crop productivity. Water and carbon dioxide fluxes from an irrigated wheat-maize double-crop field from November 2002 to October 2003 were measured using the Eddy Covariance method. Evident differences were observed between the diurnal patterns of WUE for wheat and maize. The WUE values of wheat peaked near 9, 15 and 12 mg CO 2 g H 2 O in the morning, and then decreased linearly with time and recovered in the late afternoon (4:00pm) before sunset in March, April and May, respectively. The WUE of maize increased after sunrise and retained stable values of 6, 14 and 12 mg CO 2 g H 2 O from mid-morning to mid-afternoon (10:00am Á2:00pm) and then decreased slowly with time until sunset in July, August and September, respectively. Similar patterns were observed in the RUE of wheat and maize. Over the three months of the study, averaged RUE was 1.76 g C MJ(1 for the wheat crop and 1.87 g C MJ (1 for the maize crop. A coupled photosynthesis and transpiration model was used to simulate the diurnal variations in WUE under variable climate conditions. Measurement results and sensitivity analysis show that the difference in the diurnal variation pattern in WUE between wheat and maize resulted from the different carbon fixing mechanisms of wheat and maize.

Section: Discussioncontrasting

confidence: 43%

Measurement and simulation of diurnal variations in water use efficiency and radiation use efficiency in an irrigated wheat-maize field in the North China Plain

Zhao

New Zealand Journal of Crop and Horticultural Science

et al. 2010

“…Results showed that climate change will have great impact on water requirement of winter wheat, followed by cotton and summer maize. Mo et al (2005) evaluated the spatial variations of crop yield, water consumption, and water productivity (WP) with SVATcrop growth model and found that spatial patterns of these items are closely related to water management patterns. Using the Vegetation Interface Processes (VIP) model, Mo et al (2009) evaluated crop yield, water consumption, and water use efficiency (WUE) of winter wheat and summer maize in NCP from 1951 to 2006, and their responses to future climate scenarios.…”

Section: Introductionmentioning

confidence: 99%

Modeling water requirements of major crops and their responses to climate change in the North China Plain

2015

The North China Plain (NCP) is one of the most important food production bases in China. However, its agriculture water resources are threatened by climate change. In this paper, the CROPWAT model is used to evaluate crop water requirement (CWR), crop green water requirement (CGWR), and crop blue water requirement (CBWR) for main crops in NCP (winter wheat, summer maize, cotton, millet, and soybean) with a spatial resolution of 5 arc-minute from 1961 to 2010. Their responses to future climate changes are investigated. The results show that the mean annual total CWR of the main crops during growing periods amounted to 114.68 km 3 a -1 in the past 50 years. More than 72 % of CWR to support NCP crop production is green water. The spatial distributions of CWR, CGWR, and CBWR are closely related to the planting areas and irrigation availability. Summer maize, millet, and soybean are high CGWR crops with proportions of above 84 %, while the lowest CGWR proportion is in winter wheat, 58.89 %. For climate change impacts in future, holding the crop planting system and irrigation conditions unchanged, it is projected that the total CWR in 2030s will require approximately 8.75-11.25 km 3 a -1 additional water. Results show that the CWR increase in 2030s is mainly due to the increase in temperature. Under the projected temperature in 2030s and the current rainfall scenario, total CWR, CGWR, and CBWR increments were 8.58, 1.76, and 6.82 km 3 a -1 , respectively. Nearly 80 % of the CWR increment is from the increase in CBWR. Therefore, agricultural water shortage crisis will further aggravate under future climate change scenarios in NCP, and effective water-saving measures must be taken to mitigate the negative effects of climate change.

“…Estudos que envolvem estimativas de produtividade vêm sendo desenvolvidos com uso de dados agrometeorológicos, associados ou não às imagens de sensoriamento remoto (SR), por meio de modelos agronômicos (Rudorff & Batista, 1990, 1991Mo et al, 2005). Para que os modelos possam gerar prognósticos em grandes regiões e viabilizem sua aplicação em escalas estadual ou nacional, é fundamental a sua associação aos Sistemas de Informações Geográficas (SIG) (Hartkamp et al, 1999).…”

Section: Introductionunclassified

“…Como as alterações no IAF são manifestadas na reflectância espectral dos dosséis agrícolas, este índice pode ser estimado para grandes regiões por meio de imagens de SR (Myneni et al, 1997).…”

Section: Introductionunclassified

Imagens do sensor MODIS associadas a um modelo agronômico para estimar a produtividade de soja

Rizzi

Rudorff

2007

Pesq. agropec. bras.

O objetivo deste trabalho foi estimar a produtividade de soja no Rio Grande do Sul, nas safras de 2000/2001 a 2002/2003, por meio de um modelo agronômico implementado em um Sistema de Informação Geográfica (SIG). Duas abordagens foram utilizadas: o modelo agronômico (AGRO), com valores de índice de área foliar (IAF) obtidos da literatura; e o modelo agronômico-espectral (AGROESPEC), com valores de IAF estimados a partir das imagens MODIS (moderate resolution imaging spectroradiometer). As estimativas de produtividade obtidas pelo modelo foram comparadas àquelas fornecidas pelo Instituto Brasileiro de Geografia e Estatística (IBGE), com o uso do teste t para pares de observação. Nas safras 2000/2001 e 2001/2002, não foram observadas diferenças significativas. Para 2002/2003, o modelo subestimou o valor de produtividade em 7,87 e 7,04%, nas abordagens AGRO e AGROESPEC, respectivamente, em comparação à produtividade fornecida pelo IBGE. Ambas as abordagens do modelo permitiram avaliação objetiva e quantitativa do efeito das condições meteorológicas sobre a produtividade de soja. Entretanto, o AGROESPEC forneceu estimativas mais detalhadas, no que se refere à variação espacial da produtividade, em razão do emprego dos valores de IAF estimados a partir das imagens MODIS.