Essay Example on The forecasting of yield changes based on remote sensing data

Introduction In the framework of this paper we ll consider the forecasting of yield changes based on remote sensing data Projections of climate change impacts on crop yields are subject to uncertainties and quantification of such uncertainty is essential for the effective use of the projection results for adaptation and mitigation purposes Variance decomposition of the yield projections showed that uncertainty in the projections caused by climate and crop models is likely to change with prediction period and climate change uncertainty generally had a larger impact on projections downscaled climate projections had significant bias that can introduce significant uncertainties in yield projections Therefore they have to be bias corrected before use Using climate projections to drive process based or statistical crop models to predict crop yield is the main way of performing impact assessments Lobell et al 2008 Lobell and Burke 2010 This highlights the importance of quantifying the uncertainties in crop yield prediction that are caused by using different climate projections and crop models which was neglected in many impact assessments and has only recently been emphasized 

Measuring factors which is related to weather by growth stage is particularly important for yield prediction under climate change Yield response models based on weather related variables measured by crop growth stage could be used for different planting dates According to a Pew Research Center survey climate change inflicts significant harm one of which is drought While droughts can have different causes depending on the area of the world and other natural factors the majority of scientists have started to link more intense droughts to climate change That's because greenhouse gas emissions released into the air caused increase of air temperatures in it is turn more moisture evaporates from land and lakes rivers and other bodies of water Furthermore warmer temperature can increase evaporation in plant soils which also can affects plant life and can reduce rainfall even more In any case the information about the expected drought level of harvest the causes of possible losses and the level of losses is of interest of agricultural producers themselves Loss of crops and a decrease in the quality of products from pests reach 30 or more Therefore long term and operational forecasting of the appearance of pests at the level of permissible thresholds of damage and the organization of operational protection of the production process by available technological means is of great importance Importance is the forecast for optimizing the costs of acquiring the necessary pesticides their transportation and storage etc 

Raw images are available on a daily basis but their use involves considerable extra processing Time series of MODIS imagery provide near real time continuous and relatively high 2 resolution data on which the assessment of drought development and severity in regions with scarce and inaccurate on the ground meteorological observations could be based In this study I will employ a statistical and a process based crop model driven by drought projections to evaluate the impact of climate change on maize and wheat in Hungary I will attempt to partition the variance of the simulated maize and wheat yields to analyze the contribution of individual sources of variation The objectives are to predict the potential impact of future climate change on maize and wheat production and to analyze how the prediction uncertainty varies with choice of crop models and the use of climate projections The goal set the following objectives to determine the place of forecasting in the system of management and planning of agricultural production to substantiate the role of forecasting crop yields in scenario planning and management of agricultural production to provide a scientific justification for the nature of the dynamics of crop yields of crops in general and cereals in particular justify the cyclic properties of time series of crop yields determine the nature of the interaction between the main factors of crop yields and the resultant indicator to offer the most preferable mechanism for forecasting the yield of maize wheat for the medium term period refine the system of indicators to assess the accuracy of time series models and grain crop yield forecasts