Scarcity of rainfall and limited irrigation water resources is the main challenge for agricultural expanding policies and strategies. At the same time, there is a high concern to increase the area of wheat cultivation in order to meet the increasing local consumption. The big challenge is to incerese wheat production using same or less amount of irrigation water. In this trend, the study was carried out to analyze the sensitivity of wheat yield to water deficit using remotely sensed data in El-Salhia agricultural project which located in the eastern part of Nile delta. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) were extracted from Landsat 7. Water Deficit Index (WDI) used both LST minus air temperature (Tair) and vegetation index to estimate the relative water status. Yield response factor (ky) was derived from relationship between relative yield decrease and relative evapotranspiration deficit. The relative Evapotranspiration deficit was replaced by WDI. Linear regression was found between predicted wheat yield and actual wheat yield with 0.2?6, 0.025, 0.252 and 0.76 as correlation coefficient on 30th of Dec. 2012, 15th of Jan. 2013, 16th of Feb. 2013 and 20th of Mar. 2013 respectively. The main objective of this study is using a combination between FAO 33 paper approach and remote sensing techniques to estimate wheat yield response to water.
About 21% of the world’s food depends on the wheat crop, which grows on 200 million hectares of farmland worldwide. Most of developing countries including Egypt are wheat importers. About 81% of wheat in the developing world is produced and utilized within the same country, if not the same community [
Agriculture is the largest water consuming sector. It faces competing demands from other sectors, such as the industrial and the domestic sectors. With an increasing population and less water available for agricultural production, the food security for future generations is at stake. The great challenge of the agricultural sector is to produce more food from less water, which can be achieved by increasing Crop Water Productivity (CWP) [
Yield is defined as the marketable part of the total above ground biomass production; for wheat, maize and rice total grain yield is considered, and for cotton the total lint yield and total seed yield. Unfortunately, very few sources give the moisture content at which the yield is measured, which inevitably means an error that exists in the final results [
Crop development and growth are subject to drought stress at different stages of the growth cycle, which results in differences in composition of yield components. This interrelationship was also evident when the role of grain number and weight was compared in yield determination in winter and spring wheat (Triticum aestivum L.) [
WDI = 1 ? ETa/PET (1)
where: ET (mm/day) is the product of an uptake coefficient (α, mm/day) and available water (θ ? θWP) when ET is less than PET (mm/day) [
Productivity response to water stress is different for each crop and this response is expected to vary with the climate. Therefore, the critical values of WDI should be determined for a particular crop in different climates and soils to use it in yield prediction and irrigation scheduling. Many satellite data were used to calculate WDI. [
El-Salhia project is located at the eastern part from Nile Delta. It is bounded by 30˚22'35" and 30˚31'19" latitudes and 31˚55'24" and 32˚02'38" longitudes as shown in (
Remote sensing provides spatial coverage by measurement of reflected and emitted electromagnetic radiations, across a wide range of wavebands, from the earth’s surface and surrounding atmosphere. Landsat ETM+ imageries, (path 176/row 039) around 10 a.m. local time with 30 meter ground resolution, on 30th of Dec. 2012, 15th of Jan. 2013, 16th of Feb. 2013 and 20th of Mar. 2013 were used in the current study to estimate LST, NDVI and WDI.
For landsat ETM+ data, the recorded digital numbers (DN) were converted to radiance units (Rad) using the calibration coefficients specific for each band.
Radiance = Gain* DN+ offset (2)
Surface emissivity (Eo) was estimated from the NDVI using the empirical equation developed from raw data on NDVI and thermal emissivity [
Eo = 0.9932 + 0.0194 ln NDVI (3)
The radiant temperature (To) can be calculated from band 6 radiance (Rad6) using calibration constants K1 = 666.09 and K2 = 1282.71 [
To = K2/ln((K1/Rad6) + 1) (4)
The resulting temperature (Kelvin) is satellite radiant temperature of the viewed Earth atmosphere system, which is correlated with, but not the same as, the surface (kinetic) temperature. The atmospheric effects and surface thermal emissivity have to be considered in order to obtain the accurate estimate of surface temperature from satellite thermal data [
T = To/Eo (5)
[
WDI = (dT − dTL13)/(dTL24 − dTL13) (6)
where: dT is the measure of surface subtracting air temperature at a particular percent cover, dTL13 is the surface minus air temperature determined by the line from points 1 to 3 for the percent cover of interest (“wet” line), and dTL24 is the temperature difference on the line formed between points 2 and 4 (“dry” line). Graphically, WDI can be viewed as the ratio of the distances AB to AC in the previous figure. As the WDI considers evaporation from a soil surface as well as the crop, it can be interpreted as a measure of the amount of ETa occurring relative to PET (Equation (1)). While WDI could be used to estimate ET, it does not provide a direct measure of crop water stress. As an index, it is vary according to soil-water evaporation as well as crop transpiration. The crop begins to experience levels of stress when the WDI falls to the right of a line formed between points 1 and 4 [
The major importance in production planning is the yield response to water deficit. The response of yield to water supply is quantified through the yield response factor (ky) which relates relative yield decrease to relative evapotranspiration deficit. Water deficit of a given magnitude, is expressed in the ratio ETa and PET, may either occur continuously over the total growing period of the crop or it may occur during any one of the individual growth periods. The yield response to water deficit in different individual growth periods has a major importance in the scheduling of limited supply in order to obtain highest yield. Generally, crops are more sensitive to water deficit during emergence, flowering and early yield formation than early (vegetative, after establishment) and late growth periods (ripening) [
(1 − Ya/Ym) = ky (1 − ETa/PET) (7)
where: Ya is actual harvested yield; Ym is maximum harvested yield; Ky is yield response factor; ETa is actual evapotranspiration; PET is potential evapotranspiration. Relative Evapotranspiration deficit could be replaced by WDI. From equation (1 and 7) remote sensing can take a place in FAO 33 equation as follows.
(1 − Ya/Ym) = Ky (WDI) (8)
WDI is a function of ETa to PET ratio [
WDI has been developed for the reference crop as a generic index for quantifying crop water stress for various crops. It explores how reliable of water stress estimations would be for various crops. WDI represents the suffering of crop from water shortage or/and thermal stress. WDI in the study area varied from stage to another and from year to year. It is affected by applied irrigation system, soil type and climatic conditions.
The minimum values of WDI for wheat in study area were 0.08, 0.03, 0.03, and 0.02 and maximum values were 0.51, 0.27, 0.2, and 0.14 on 30th of Dec. 2012, 15th of Jan. 2013, 16th of Feb. 2013 and 20th of Mar. 2013 respectively. The values of WDI were high in the first stage because the canopy was not 100% coverage and the temperature of soil was higher than temperature of canopy.
Actual wheat yield can only be determined by accurately measuring the area and determining the weight of grain harvested. Environmental stress always reduces Ym. In (
crop varieties, well-adapted to the growing environment and grown under a high level of crop management [
The values of WDI were higher in establishment and vegetative stages than flowering and yield formation because the soil was not fully covered with canopy and the temperature of soil was higher than temperature of canopy. This factor reduces the accuracy of this method in partial canopy coverage case. The predicted wheat
yield was plotted against actual yield, the relations were varied during different phenological stages where R2 = 0.2−6, 0.03, 0.3 and 0.76. There was no correlation in establishment and vegetative stages but it improved in flowering stage and it was good in yield formation stage. The combination of FAO 33 paper approach and remote sensing techniques is a good idea to estimate yield response to water but it needs to improve.
M. A. El-Shirbeny,A. M. Ali,A. Rashash,M. A. Badr, (2015) Wheat Yield Response to Water Deficit under Central Pivot Irrigation System Using Remote Sensing Techniques. World Journal of Engineering and Technology,03,65-72. doi: 10.4236/wjet.2015.33B011