While many Arab countries face the risk of social unrest as a result of the food crisis, which is taking shape day by day as a result of the effects of the Ukraine war on exports of basic foodstuffs, particularly cereals, as well as climatic changes, particularly desertification and drought, the United Nations has expressed its concern about a global hunger wave. The United Nations Food and Agriculture Organization (FAO) warned that the conflict in Ukraine threatens Egypt's food security, as Egypt is the world's first importer of wheat, relying on Russia and Ukraine for 85 percent of its wheat imports.

Regarding food security in Arab countries, it is necessary to mention facts, most notably limited agricultural land, primitive methods and means of production and social changes that made agricultural professions not receive sufficient demand from citizens. These reasons have prevented the development of the agricultural, animal and plant sectors and kept pace with the population and the basic daily needs of citizens. Egypt is a first-class agricultural country, but it has become the largest wheat importer with 17 million tons per year, primarily from Russia and Ukraine. 

Wheat, which grows on 200 million hectares of farmland, provides 21% of the world's food. Egypt imports wheat like most developing countries. About 81% of developing world wheat is produced and used within the same country, if not the same community (FAO website). Global population growth puts pressure on limited freshwater resources. 

Agriculture uses the most water. Other sectors, such as industrial and domestic, have competing demands. The growing population and less water for agriculture threaten future food security. Increasing Crop Water Productivity (CWP) helps farmers produce more food with less water [1]. Agronomic research and better land and water management have increased CWP over time. Water shortages reduce cereal yields globally and, in the Mediterranean, [2]. Crop productivity depends on total and seasonal precipitation [3,4].

Although wheat is a low-water consumption, it's sensitive to water stress in some stages. Total grain yield is considered for wheat, maize and rice; for cotton, total lint yield and seed yield. Sadly, few sources provide the moisture content at which the product is measured, resulting in an error in the final results [1].

Drought stress affects crop development and yield composition at different crop growth stages. This interrelationship was also evident in winter and spring wheat (Triticum aestivum L.) yield determination. Water deficit from booting to anthesis reduces floret set due to decreased shoot water status and increased abscisic acid [5]. Reduce grain-bearing tillers and grain sets [6]. On the other hand, each crop's productivity response to water stress varies with the climate. 

The rate of return on an investment of one water unit is referred to as water productivity. Increasing water productivity may generate more food, more money, better lifestyles and more excellent ecological services while using less water. From the scale of the field to that of the basin, there is potential for the increased crop, livestock and fishery water production. These include soil–water conservation, supplemental irrigation, deficit irrigation, precision irrigation and water collecting from existing bodies of water. The amount of water produced can be affected by factors such as the quality of the soil, methods for preventing and treating pests and diseases, the types of crops grown and access to markets.

On the other hand, there are several reasons to be skeptical about how far and how easily advances in water production will spread. In highly productive regions, crop water productivity is already relatively high and yield improvements (per unit of land area) may not usually translate to increases in water productivity. Water reuse within an irrigated region or basin can compensate for apparent water quantity losses on a field size; however, this might come at the water's quality. In the past, crop breeding has increased water productivity, most notably by increasing the harvest index. However, such significant increases are not predicted to be made in the future. Farmers and water managers do not have access to the instruments necessary to significantly increase water output. A better understanding of the biophysical and socioeconomic aspects of the field, farm and basin is required to increase water output.

The ratio of the net benefits derived from agricultural, forestry, fisheries, livestock and mixed pastoral systems to the total amount of water consumed defines water productivity. In its most general sense, it denotes the provision of additional food, monetary value, means of subsistence and environmental advantages for each unit of water used. Economic water productivity refers to the value that is obtained for each unit of water that is consumed. In contrast, biological water productivity refers to agricultural production to water consumption. This has been utilized to establish a connection between the amount of water used in agriculture and issues about nutrition, jobs, welfare and the environment. The amount of water that is available or lost is the factor that makes up the denominator in the equation for water productivity. The ET assimilation into a product flow to a point where it cannot be reused and pollution is one of the four processes that deplete water [7,8].

Food consumption will increase by 60% by 2050, while fiber demand will increase by 80-95 percent, putting strain on water quality and quantity and productive soils. Agriculture is the world's largest water user (70% of total water withdrawn annually), but new water supplies are limited and competition increases. Agriculture must provide more water to industry, electricity and urban expansion. Unpredictable rainfall, increased weed growth as CO₂ levels rise and more pests may stymie agricultural productivity. Agriculture's 14 percent contribution to global greenhouse gas emissions will likely increase unless farming practices are changed.

The primary purpose of this study is to evaluate the wheat water productivity based on determining the sensitivity of wheat yield to water consumption by integrating data collected from remotely sensed and ground-based.

Study Area Location

The study area is located in the El-Salhia project and encompasses 13,800 hectares (Figure 1). This project makes use of drip irrigation and central pivots. The project contains more than 120 pivots. Each pivot is 450 meters long and covers an area of 63.6 hectares on average. Drip irrigation is used to water orchard crops. The Köppen Climate Classification System classifies the research region as Dry Arid, with precipitation less than 50% of potential evapotranspiration. The average annual temperature exceeds 18°C. The yearly precipitation totals 20 mm. In January, the average rainfall is 6.9 mm. The average temperature in June is 34.6 °C, while the average temperature in January is 19.0 °C. In January, the minimum temperature is 8.0 degrees Celsius, while the maximum temperature is 21.5 degrees Celsius in August [7,9-14].

Data Accessibility

Satellite Data: By utilizing remote sensing, one is able to investigate the dynamics of the environment at either the local or the global scale. In order to achieve a deeper comprehension of space and time, the photographs that were taken by satellites such as Landsat, Sentinel and Spot are evaluated, classified and investigated [15,16]. Satellite data collected by MODIS Terra and Sentinel-2 between November 1, 2019 and April 30, 2020, have been utilized.

Daily LST and emissivity data are provided by the MOD11A1 V6 and MOD11 L2. The daily land surface temperature (LST) at 1 km was determined with the use of MODIS data. While Sentinel-2 is an imaging mission that uses many spectral bands and has a high resolution. The resolution of the vegetation indices that are produced using Sentinel data is 10 meters and the time resolution is 5 days.

Field Data

In the region under investigation, field data about wheat production and crop type have been obtained. During the growing season, there are twenty pivot systems used to cultivate the wheat. The data on yield ranged from 15.1 ardab/fed to 20.5 ardab/fed in its range. Wheat, sugar beets and potatoes were the most prevalent types of crops grown in the field in the region under investigation.

Figure 1: Study Area Location Map

Water Productivity

The WP (kg/m3) represents the benefit from the water unit. It changed depending on the meteorological circumstances, the kind of crop and the amount of water that was available.

Where,

Yg is the grain yield in grams per square meter

ETa is the actual amount of water that the crop uses (mm)

There is a mathematical connection between the CWSI and the amount of water used by plants [18]. There is no consumption of water when the CWSI is equal to one. Eta = ETc if CWSI = 0 (there is no water shortage). CWSI and ETc are both useful tools for calculating the amount of water used in agriculture.

Eta = (1-CWSI) * ETc

2

CWSI was invented by Idso et al. [17], Jackson et al. [18], proposed practical and theoretical techniques to CWSI.

In this equation, T represents the LST-Tair interval, Tm represents the lowest LST-Tair variation and Tx represents the maximum LST-Tair interval. CWSI is a percentage since all variables have equal units. The climatic and well-watered conditions that prevail during ET are what determine the lower boundary of dT. After states that do not transpire, dT will increase when ET is stopped because of a lack of water. CWSI is located between no scarcity, represented by zero and extreme scarcity, represented by one.

The use of crop water by wheat as compared to that of a grass that was adequately watered. ETo and Kc are dependent on the local climate, the volume of the canopy, the availability of water and nutrients and the absence of pests and diseases.

ETC = KC * ETO

4

For the purpose of determining ETo, the FAO-Penman-Monteith (FPM) model was utilized. In Egypt, several people make use of the model and its results are well recognized and approved.

Kc is proportional to the value (commonly varied from 0.3 to 1.2). Equation (5) was applied by El-Shirbeny et al. [7,9], in order to demonstrate the connection between Kc and NDVI in wheat [11].

NDVIdv is the difference between the lowest and maximum plant NDVI rates, while NDVImv is the lowest plant NDVI rate. Where 1.2 denotes the greatest Kc rate under Egyptian circumstances, NDVIdv is the gap between the lowest and highest plant NDVI rates.

Yield Response Factor (Ky)

The production planning process absolutely requires the yield response to water shortage data. Through the yield response factor (ky), the decline in yield is connected to the shortfall in evapotranspiration. It is possible for a particular water deficit, which is demonstrated by the ratio of ETa to PET (ETc), to exist constantly or during any particular growth phase of the crop. It is essential for production to respond appropriately to water scarcity at various stages of plant growth if the output is to be maximized with constrained resources. Emerging, blooming and early yield production are not as vulnerable to water stress as early (vegetative) and late (ripening) growth. In spring wheat, flowering is more responsive than yield production, but in winter wheat, flowering is more sensitive than yield production. When attempting to quantify the effect of water stress, it is necessary to use the empirically acquired ky [17].

Where,

Ya represents actual harvested yield, Ym represents maximum harvested yield, Ky represents yield response factor, ETa represents actual evapotranspiration and PET represents potential evapotranspiration.

Wheat Water Signature

The usage pattern for water throughout the springtime. Identification of the wheat crop in the research region was accomplished by applying wheat discrimination based on satellite data. The pattern of monthly water consumption from November 2019 through April 2020 has been utilized to identify various crops based on the ways of water consumption that each crop exhibited during the season.

Several studies utilized the series of vegetation indicators to classify crops [13,18], whereas thermal signature was employed by other researchers [19]. A categorization of the crops that were found in the research region based on how much water they used can be found in this article (Figure2).

The crop water signature of the most major field crops in the region under investigation is illustrated in Figure 3. Wheat required 435 millimeters of water, beginning November 1, 2019 and ending April 30, 2020. In comparison, sugar beets and winter potatoes needed 639 and 280 millimeters of water throughout the same period. 

Figure 2: Crops Water Consumption Signature Based On Satellite Data; Green Color Represented Wheat

The planting date, growth period, time of year, meteorological conditions and the amount of water that is readily available all have a role in determining the water consumption curves. The wheat crop began to use more water starting in December 2019 and continued until February 2020, representing the period of maximum vegetative growth. After that, the crop began to gradually reduce the amount of water it used until harvest time. Up to April, the sugar beets will continue to develop and will require an increasing quantity of water. After April, the sugar beets will be able to continue their existence until they are harvested. Because winter potatoes are planted in September and harvested in January or February of each year, the consumption of water began before the time period studied and stopped during the winter season when the crop was harvested. This is because the crop is harvested in the winter season.

Crop Water Consumption

To keep track of the amount of water used by the crops planted in the research area, actual monthly evapotranspiration (ETa) has been developed. The ETa varies depending on the type of crop being grown, the growth stage the plant is in, as well as the season and the temperature. Water usage rises throughout the spring and summer months, particularly in areas with dense plant cover. Wheat, sugar beets and potatoes are the primary agricultural products produced in the region under investigation. The water requirements of these three crops are distinct, as are the cultivation dates and phenological stages at which they are each harvested.

The highest rate of agricultural water use in the region under investigation was reported in April and it was 170 mm/month. Although the lowest water use by the crop was registered in December, the average monthly rainfall was still 95 millimeters (Figure 4). When the weather in the region is constant, the primary factor determining the amount of water used by crops is the phenological stage of the harvest and water availability.

Figure 3: Major Crop’s Water Consumption Signature Based On Monthly ETa During the Winter Season for Wheat, Sugar Beet and Potatoes

Figure 4: Monthly Actual Evapotranspiration (ETa) for The Study Area During the Study Period from Nov. 2019 to Apr. 2020

Figure 5: The Relationship between Yield (kg/ha) and Wheat Water Consumption (m3/ha)

Water Productivity

Water productivity is a prominent topic in the water and agricultural industries, particularly in water-stressed countries. Productivity is defined as yield per unit of water. Removing water from low-productivity applications now will ensure that water is available in the future to meet increased demand for food, fodder, fiber and other purposes, minimizing the chance of conflict. While SDG 6.4 aims to promote water-use efficiency across all sectors, maintain sustainable freshwater withdrawals and supplies to alleviate water scarcity and reduce the number of people suffering from water scarcity by 2030.

Water production emerged from a variety of disciplines. Crop physiologists initially defined water use efficiency as carbon absorbed and crop yield per unit of transpiration, then as biomass or marketable output per unit of ET. Water consumption efficiency indicates how well water is delivered to crops and how much is wasted. This concept is insufficient since it fails to address the benefits or the fact that irrigation water is often reused [7-12,14,20]. Today, water productivity covers the benefits and costs of agriculture in both land and aquatic ecosystems.

When water is restricted compared to other production resources, WP should be enhanced. Improving agricultural water productivity can assist meet rising food requirements from a larger, wealthier and more urbanized population in the face of water scarcity, respond to efforts to re-allocate water from farmland to cities and contribute to poverty reduction and economic growth. More effective water utilization equals better nutrition, income and employment for the rural poor. High water productivity lowers investment costs by decreasing water withdrawal. Increased water productivity reduces irrigated and rainfed systems' water and land requirements. Increasing water productivity is a critical answer to developing water constraints, particularly the obligation to leave enough water in rivers to maintain ecosystems while also meeting the demands of communities and companies [21-24].

Globally, the additional water required for agriculture is determined by increases in water productivity. Agriculture ET might triple in 50 years if no breakthroughs are made. With qualified expenditures in water production, global ET might increase by 20–30%. Irrigation systems must generate more with less water. Irrigation allocations are diminishing in many river basins because of growing demands from cities and the environment. Farmers must increase water production if they are to continue producing.

The main link between yield (kg/ha) and wheat water consumption (m3/ha), which is illustrated in Figure 5, is a polynomial equation of the second degree. The determination coefficient (R2) is as high as 0.43. In the region under research, there is a variance of yield that reaches up to 25 %, although there is only a variety of wheat water consumption that reaches up to 10 %.

The linear equation represents the relationship between yield response factor and yield kg/ha and a very strong determination coefficient R2 equals 0.99 (Figure 6). The variation of the yield response factor properly responded to the variation in wheat yield.

The link between the yield response factor and wheat water consumption m3/ha is represented by a linear equation and the determination coefficient R2 for this equation is as high as 0.41 (Figure 7). When compared to its reaction to wheat yield, the change in the water consumption of wheat had a moderate effect on the variance of the yield response factor.

The water productivity varied from 1.32 to 1.66 with an average of 1.47 kg/m³. The yield response factor has a larger reaction to wheat yield than wheat water consumption, which might be due to the fact that measured output can vary by up to 25%, whilst wheat water consumption can only vary by up to 10%.

Figure 6: The Relationship Between Yield Response Factor and Yield (kg/ha).

Figure 7: The Relationship Between Yield Response Factor and Wheat Water Consumption (m3/ha)

The primary challenge is to increase wheat production while retaining or lowering irrigation water consumption. In this respect, research was conducted at the El-Salhia agricultural project in the Nile delta to explore wheat water productivity based on determining the sensitivity of wheat output to water consumption using remotely sensed data. The remote sensing data were utilized to estimate the actual evapotranspiration (ETa) from November 1, 2019, to April 30, 2020. The sequence of monthly ETa has been employed as a crop water consumption signature to categorize the crops in the research region. The crop water consumption signature is influenced by both the crop phenological signature and the crop thermal signature. Wheat, sugar beet and potatoes are the most prevalent crops in the study region, using 435, 639 and 280 mm, respectively. The cumulative ETa reflected wheat water use throughout the season, whereas wheat yield was obtained from 20 pivot irrigation systems. The yield ranged from 5.4 to 7.32 tons/ha. Although there is a range in measured yield of up to 25%, there is only a variation in wheat water consumption of up to 10%. Water productivity ranged between 1.32 and 1.66 kg/m³, with an average of 1.47 kg/m³. Wheat production is more responsive to the yield response factor than wheat water consumption. Even though there is a need for and potential for, increased water productivity, little progress has been made in this area. However, continued research that examines water productivity gives recommendations for future growth paths and highlights significant hurdles that must be addressed to boost agricultural water production in Egypt and other arid countries is essential.

1. Zwart, S.J. and W.G.M. Bastiaanssen. "Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize." Agricultural Water Management, vol. 69, 2004, pp. 115-133, https://doi.org/10.1016/j.agwat.2004.04.007.

2. Acevedo, E.H. et al. "Wheat production in mediterranean environments." Wheat Ecology and Physiology of Yield Determination, edited by E.H. Satorre and G.A. Slafer, Food Products Press, 1999, pp. 295-323.

3. Loss, S.P. and K.H.M. Siddique. "Morphological and physiological traits associated with wheat yield increases in mediterranean environments." Advances in Agronomy, vol. 52, 1994, pp. 229-276, https://doi.org/10.1016/S0065-2113(08)60625-2.

4. Slafer, G.A. "Genetic basis of yield as viewed from a crop physiologist’s perspective." Annals of Applied Biology, vol. 142, 2003, pp. 117-128, https://doi.org/10.1111/j.1744-7348.2003.tb00237.x.

5. Westgate, M.E. et al. "Water status and ABA content of floral organs in drought-stressed wheat." Australian Journal of Plant Physiology, vol. 23, 1996, pp. 763-772.

6. Foulkes, M.J. et al. "Identifying physiological traits associated with improved drought resistance in winter wheat." Field Crops Research, vol. 103, 2007, pp. 11-24.

7. El-Shirbeny, M.A. et al. "Changes in irrigation water consumption in the Nile delta of Egypt assessed by remote sensing." Arabian Journal of Geosciences, vol. 8, no. 12, 2015, pp. 10509-10519.

8. El-Shirbeny, M.A. et al. "Monitoring agricultural water in the desert environment of new valley governorate for sustainable agricultural development: A case study of Kharga." Euro-Mediterranean Journal for Environmental Integration, vol. 6, no. 2, 2021.

9. El-Shirbeny, M.A. et al. "Assessment of the mutual impact between climate and vegetation cover using NOAA-AVHRR and landsat data in egypt." Arabian Journal of Geosciences, vol. 7, no. 4, 2014, pp. 1287-1296.

10. El-Shirbeny, M.A. et al. "Crop water requirements in egypt using remote sensing techniques." Journal of Agricultural Chemistry and Environment, vol. 3, no. 2, 2014, p. 57.

11. El-Shirbeny, M.A. et al. "Assessment of wheat crop coefficient using remote sensing techniques." World Research Journal of Agricultural Sciences, vol. 1, no. 2, 2014.

12. El-Shirbeny, M.A. et al. "Estimation of potential crop evapotranspiration using remote sensing techniques." Proceedings of the 10th International Conference of AARSE, 2014, pp. 460-466.

13. El-Shirbeny, M.A. and B. Abdellatif. "Reference evapotranspiration borders maps of Egypt based on kriging spatial statistics method." GEOMATE Journal, vol. 13, no. 37, 2017.

14. El-Shirbeny, M.A. and S.M. Saleh. "Actual evapotranspiration evaluation based on multi-sensed data." Journal of Arid Agriculture, 2021, pp. 95-102.

15. Aboelghar, M. et al. "Retrieving leaf area index from spot4 satellite data." The Egyptian Journal of Remote Sensing and Space Science, vol. 13, no. 2, 2010, pp. 121-127. 

16. Aboelghar, M. et al. "Using spot data and leaf area index for rice yield estimation in Egyptian Nile delta." The Egyptian Journal of Remote Sensing and Space Science, vol. 14, no. 2, 2011, pp. 81-89.

17. Doorenbos, J. and O.W. Kassam. Yield Response to Water. FAO, 1979. FAO Irrigation and Drainage, paper no. 33.

18. Ali, Abdelraouf M. et al. "Integrated method for rice cultivation monitoring using sentinel-2 data and leaf area index." The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, 2021, pp. 431-441.

19. Tolba, R.A. et al. "Rice acreage delineation in the nile delta based on thermal signature." Earth Systems and Environment, vol. 4, no. 1, 2020, pp. 287-296.

20. El-Shirbeny, M.A. et al. "Estimation of crops water consumptions using remote sensing with case studies from Egypt." Conventional Water Resources and Agriculture in Egypt, 2019, pp. 451-469.

21. El-Shirbeny, M.A. et al. "Evaluation of hargreaves based on remote sensing method to estimate potential crop evapotranspiration." GEOMATE Journal, vol. 11, no. 23, 2016, pp. 2143-2149.

22. El-Shirbeny, M.A. and K. Abutaleb. "Sentinel-1 radar data assessment to estimate crops water stress." World Journal of Engineering and Technology, vol. 5, no. 2, 2017, pp. 47-56.

23. El-Shirbeny, M.A. et al. "Monitoring of actual evapotranspiration using remotely sensed data under modern irrigation systems." Journal of Geography, Environment and Earth Science International, vol. 12, no. 4, 2017.

24. Mohamed, E.S. et al. "Mapping soil moisture and their correlation with crop pattern using remotely sensed data in arid region." The Egyptian Journal of Remote Sensing and Space Science, vol. 23, no. 3, 2020, pp. 347-353.