scholarly journals Analysis of meteorological variations on wheat yield and its estimation using remotely sensed data. A case study of selected districts of Punjab Province, Pakistan (2001-14)

2017 ◽  
Vol 12 (3) ◽  
Author(s):  
Rafia Mumtaz ◽  
Shahbaz Baig ◽  
Iram Fatima
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Crop Yield ◽  
Vegetation Index ◽  
Wheat Yield ◽  
Least Square ◽  
Wheat Crop ◽  
Main Challenge ◽  
Normalised Difference Vegetation Index ◽  
The Impact

Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI) as a predictor for yield estimates. For NDVI data (2001-14), the NDVI product of Moderate Resolution Imaging spectrometer (MODIS) 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO) data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF) has been applied prior to linear regression. The coefficients of determination (<em>R</em><sup>2</sup>) between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the method is capable of providing yield estimates with competitive accuracies prior to crop harvest, which can significantly aid the policy guidance and contributes to better and timely decisions.

Corrigendum to: Influence of climatic factors on variation in the Normalised Difference Vegetation Index in Mongolian Plateau grasslands

2018 ◽  
Vol 40 (2) ◽  
pp. 205
Author(s):  
Xu-Juan Cao ◽  
Qing-Zhu Gao ◽  
Ganjurjav Hasbagan ◽  
Yan Liang ◽  
Wen-Han Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Limiting Factor ◽  
Spatial And Temporal Variability ◽  
Climate Factors ◽  
Mongolian Plateau ◽  
Normalised Difference Vegetation Index

Climate change will affect how the Normalised Difference Vegetation Index (NDVI), which is correlated with climate factors, varies in space and over time. The Mongolian Plateau is an arid and semi-arid area, 64% covered by grassland, which is extremely sensitive to climate change. Its climate has shown a warming and drying trend at both annual and seasonal scales. We analysed NDVI and climate variation characteristics and the relationships between them for Mongolian Plateau grasslands from 1981 to 2013. The results showed spatial and temporal differences in the variation of NDVI. Precipitation showed the strongest correlation with NDVI (43% of plateau area correlated with total annual precipitation and 44% with total precipitation in the growing season, from May to September), followed by potential evapotranspiration (27% annual, and 30% growing season), temperature (7% annual, 16% growing season) and cloud cover (10% annual, 12% growing season). These findings confirm that moisture is the most important limiting factor for grassland vegetation growth on the Mongolian Plateau. Changes in land use help to explain variations in NDVI in 40% of the plateau, where no correlation with climate factors was found. Our results indicate that vegetation primary productivity will decrease if warming and drying trends continue but decreases will be less substantial if further warming, predicted as highly likely, is not accompanied by further drying, for which predictions are less certain. Continuing spatial and temporal variability can be expected, including as a result of land use changes.

scholarly journals Quantifying Changes in Plant Species Diversity in a Savanna Ecosystem Through Observed and Remotely Sensed Data

2020 ◽  
Vol 12 (6) ◽  
pp. 2345
Author(s):  
Lazarus Chapungu ◽  
Luxon Nhamo ◽  
Roberto Cazzolla Gatti ◽  
Munyaradzi Chitakira
Keyword(s):  
Climate Change ◽  
Species Diversity ◽  
Plant Species ◽  
Vegetation Index ◽  
Shannon Index ◽  
Plant Species Diversity ◽  
Impact Of Climate Change ◽  
Normalised Difference Vegetation Index ◽  
The Impact ◽  
Index Of Diversity

This study examined the impact of climate change on plant species diversity of a savanna ecosystem, through an assessment of climatic trends over a period of forty years (1974–2014) using Masvingo Province, Zimbabwe, as a case study. The normalised difference vegetation index (NDVI) was used as a proxy for plant species diversity to cover for the absence of long-term historical plant diversity data. Observed precipitation and temperature data collected over the review period were compared with the trends in NDVI to understand the impact of climate change on plant species diversity over time. The nonaligned block sampling design was used as the sampling framework, from which 198 sampling plots were identified. Data sources included satellite images, field measurements, and direct observations. Temperature and precipitation had significant (p < 0.05) trends over the period under study. However, the trend for seasonal total precipitation was not significant but declining. Significant correlations (p < 0.001) were identified between various climate variables and the Shannon index of diversity. NDVI was also significantly correlated to the Shannon index of diversity. The declining trend of plant species in savanna ecosystems is directly linked to the decreasing precipitation and increasing temperatures.

Influence of climatic factors on variation in the Normalised Difference Vegetation Index in Mongolian Plateau grasslands

2018 ◽  
Vol 40 (2) ◽  
pp. 91 ◽  
Author(s):  
Xu-Juan Cao ◽  
Qing-Zhu Gao ◽  
Ganjurjav Hasbagan ◽  
Yan Liang ◽  
Wen-Han Li ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Vegetation Index ◽  
Climatic Factors ◽  
Growing Season ◽  
Limiting Factor ◽  
Spatial And Temporal Variability ◽  
Climate Factors ◽  
Mongolian Plateau ◽  
Normalised Difference Vegetation Index

Climate change will affect how the Normalised Difference Vegetation Index (NDVI), which is correlated with climate factors, varies in space and over time. The Mongolian Plateau is an arid and semi-arid area, 64% covered by grassland, which is extremely sensitive to climate change. Its climate has shown a warming and drying trend at both annual and seasonal scales. We analysed NDVI and climate variation characteristics and the relationships between them for Mongolian Plateau grasslands from 1981 to 2013. The results showed spatial and temporal differences in the variation of NDVI. Precipitation showed the strongest correlation with NDVI (43% of plateau area correlated with total annual precipitation and 44% with total precipitation in the growing season, from May to September), followed by potential evapotranspiration (27% annual, and 30% growing season), temperature (7% annual, 16% growing season) and cloud cover (10% annual, 12% growing season). These findings confirm that moisture is the most important limiting factor for grassland vegetation growth on the Mongolian Plateau. Changes in land use help to explain variations in NDVI in 40% of the plateau, where no correlation with climate factors was found. Our results indicate that vegetation primary productivity will decrease if warming and drying trends continue but decreases will be less substantial if further warming, predicted as highly likely, is not accompanied by further drying, for which predictions are less certain. Continuing spatial and temporal variability can be expected, including as a result of land use changes.

scholarly journals Assessment of climate change impact on crop yield and irrigation water requirement of two major cereal crops (rice and wheat) in Bhaktapur district, Nepal

2016 ◽  
Vol 8 (2) ◽  
pp. 320-335 ◽  
Author(s):  
Lajana Shrestha ◽  
Narayan Kumar Shrestha
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Irrigation Water ◽  
Wheat Yield ◽  
Circulation Model ◽  
Fertilizer Application ◽  
Water Requirement ◽  
Cereal Crops ◽  
Irrigation Water Requirement ◽  
The Impact

Rice and wheat are major cereal crops in Nepal. Climate change impacts are widespread and farmers in developing countries like Nepal are among the most vulnerable. A study was carried out to assess the impact of climate change on yield and irrigation water requirement of these cereal crops in Bhaktapur, Nepal. Laboratory and soil-plant-air-water analysis showed silt-loam being the most dominant soil type in the study area. A yield simulation model, AquaCrop, was able to simulate the crop yield with reasonable accuracy. Future (2030–2060) crop yield simulations, on forcing the Providing Regional Climates for Impacts Studies (PRECIS) based on regional circulation model simulation indicated decreased (based on HadCM3Q0 projection) and increased (based on ECHAM5 projection) yield of monsoon rice for A1B scenario, and rather stable yield (for both projection) of winter wheat. Simulation results for management strategies indicated that the crop yield was mainly constrained by water scarcity and fertility stress emphasizing the need for proper water management and fertilizer application. Similarly, a proper deficit irrigation strategy was found to be suitable to stabilize the wheat yield in the dry season. Furthermore, an increase in fertilizer application dose was more effective in fully irrigated conditions than in rainfed conditions.

A fusion of UAVs, digital cameras and micrometeorology to quantify the link between phenology and the carbon balance of a temperate peatland

2021 ◽  
Author(s):  
Gillian Simpson ◽  
Carole Helfter ◽  
Caroline Nichol ◽  
Tom Wade
Keyword(s):  
Climate Change ◽  
Carbon Balance ◽  
Vegetation Index ◽  
Natural Capital ◽  
Carbon Sink ◽  
Sink Strength ◽  
Environmental Controls ◽  
Spatial Coverage ◽  
Normalised Difference Vegetation Index ◽  
The Impact

&lt;p&gt;Peatland ecosystems are historical carbon sinks of global importance, whose management and restoration are becoming an increasingly popular approach to reach climate change targets via natural capital. However, the Net Ecosystem Exchange (NEE) of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) can exhibit substantial variability on seasonal and inter-annual timescales, with some peatlands shifting from being a sink to a source of CO&lt;sub&gt;2&amp;#160;&lt;/sub&gt;between years. This variability is due to the complex interaction between factors such as meteorology and phenology, which are both known to control a peatland&amp;#8217;s net carbon sink strength. An improved understanding of these two drivers of peatland carbon cycling is needed to allow for better prediction of the impact of climate change on these ecosystems. This task requires us to study these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE however, can be difficult to determine over shorter timescales (e.g. seasonal) and especially in peatland landscapes, which typically display strong spatial heterogeneity at the microsite scale (&lt; 0.5 m). Digital phenology cameras (PhenoCams) and Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs in particular are a more flexible, often cheaper alternative to satellite products, and can be used to collect data at the sub-centimetre scale. We employ PhenoCam imagery and UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at Auchencorth Moss, a Scottish temperate peatland. By combining this digital camera imagery with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, we seek to quantify the impact of weather and phenology on carbon balance at the site.&lt;/p&gt;

scholarly journals Sensitivity of four wheat simulation models to climate change

1995 ◽  
Vol 75 (1) ◽  
pp. 69-74 ◽  
Author(s):  
A. Touré ◽  
D. J. Major ◽  
C. W. Lindwall
Keyword(s):  
Climate Change ◽  
Spring Wheat ◽  
General Circulation ◽  
Wheat Yield ◽  
Simulation Models ◽  
Climate Scenario ◽  
Wheat Crop ◽  
Continuous Rotation ◽  
The Impact ◽  
Low Precipitation

Crop simulation models may be valuable in anticipating crop production under a changed climate. We compared four computer simulation models of wheat, crop estimation through resources and environment synthesis (CERES), erosion productivity impact calculator (EPIC), Stewart and Sinclair, for evaluating the impact of climate change on dryland spring wheat yield for continuous rotation in southern Alberta. To a varying extent, the four models showed decreases in dryland spring wheat yields due to high temperature and low precipitation. All the models except Stewart had similar sensitivity to low precipitation; however, they showed differences to high-moisture conditions. Within the range considered, the Sinclair model was the most sensitive to temperature, followed by CERES and Stewart. Only EPIC indicated optimum temperature and precipitation levels, while CERES had the most pronounced precipitation optimum. Although the CERES, Stewart and Sinclair models have different phenology submodels, they predicted similar phenological response to a doubled CO2 climate scenario generated from the Canadian Climate Center General Circulation Model for Lethbridge, AB. Growing seasons shortened by 19 d were predicted using CERES and 18 d by using the Sinclair and Stewart models. The CERES, Stewart and Sinclair models were modified to include the effect of CO2 on radiation-use-efficiency. With current atmospheric CO2 concentration in the future climate scenario, the EPIC and Stewart models predicted significant (25%) and non-significant (3%) yield increases for dryland wheat and Sinclair and CERES predicted yield losses. Higher CO2 levels may compensate for the effect of global warming; doubling CO2 from present levels in a warmer climate scenario resulted in yield increase predictions at different amplitudes using EPIC, Stewart and CERES and a slight yield decrease with Sinclair. Key words: Sensitivity, climate change, dryland spring wheat

Spatio-temporal variability in rangeland conditions associated with climate change in the Altun Mountain National Nature Reserve on the Qinghai-Tibet Plateau over the past 15 years

2015 ◽  
Vol 37 (1) ◽  
pp. 67 ◽  
Author(s):  
S. L. Liu ◽  
H. D. Zhao ◽  
X. K. Su ◽  
L. Deng ◽  
S. K. Dong ◽  
...  
Keyword(s):  
Climate Change ◽  
Vegetation Index ◽  
Nature Reserve ◽  
Driving Forces ◽  
Linear Regression Analysis ◽  
Tibet Plateau ◽  
National Nature Reserve ◽  
Rangeland Degradation ◽  
Normalised Difference Vegetation Index ◽  
The Impact

One of the focuses of global change research is on the impact of climate change on alpine vegetation. The Altun Mountain National Nature Reserve is the largest alpine desert rangeland reserve in China to protect wild endangered ungulate species. This paper aims to detect changing trends in rangeland conditions in this region. Temporal changes in the Normalised Difference Vegetation Index (NDVI) for the rangelands in the Altun Nature Reserve and its correlation with climatic variables were studied over the period from 1998 to 2012. Based on the NDVI index and using ArcGIS spatial analyst, the areas of likely rangeland degradation and areas of improved in rangeland condition were identified using linear regression analysis. The results showed that NDVI values were relatively low, varying from 0.04 to 0.1, and there existed distinct monthly changes. The highest NDVI values were exhibited in August. Generally, the NDVI showed an increasing trend over time with several annual fluctuations. High values were distributed mainly in the core area of the nature reserve. Trend analysis showed that vegetation near rivers and lakes was most likely to be degraded but, overall, the vegetation conditions improved over the 15 years of the study, which meant an improvement in the habitats of key wild ungulate species. Precipitation and temperature had a significant linear positive correlation with NDVI, which suggested that they were the main driving forces for rangeland improvement. The vegetation at the edge of the protected areas appeared degraded due to human activities.

Climate-forced changes of bio-productivity of Belorussian terrestrial ecosystems

2019 ◽  
pp. 77-88
Author(s):  
S. A. Lysenko
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Terrestrial Ecosystems ◽  
Vegetation Period ◽  
Climatic Trend ◽  
Vegetation Growth ◽  
Current Century ◽  
The Impact

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

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