Sample Research Paper on Water Requirement Satisfaction Index (WSRI)

Water Requirement Satisfaction Index (WSRI) Impact on Cereal Crop Yields

Water requirement satisfaction index (WRSI) is a crop water balance technique that handles modeling the effect of rainfall availability seasonally on crop yields. Water balance is the variance between the water lost from the soil and crops and precipitation that is experienced by the crops (Brown, 2014). During the calculation of this water balance, the water that is conserved in the soil and can be utilized by the crop is considered. WRSI indicates the degree (in percentage form) of water requirement that satisfies crops cumulatively at any given stage in any crop growing season. WRSI models are in most cases used to determine the early estimates of crop production and yield (Martin, Washington, & Downing, 2000).

For years, the WRSI has been used to measure the reduction experienced in yield per unit area because of water shortage over specific stages of the crop growth. This model does not in any other way measure other kinds of reduction of yields (Moeletsi & ME, 2012). FEWS NET handles the calculation of WRSI of most cereals like millet, maize, and sorghum in Central America and Africa. In a given season, WRSI is determined by analyzing water demand for a crop and the water supply over that crop development season. This index is calculated as a ratio of Actual Evapotranspiration (AET) to the crop water requirement (WR) in a given season.

WR is determined from the Penman-Monteith evapotranspiration (PET) which in turn uses the crop coefficient (Kc) in changing the development stage of a given crop. AET is an actual representation of the water that is used by the crop from the soil (Brown, 2014). Considering different types of crops (cereals), when the water content in the soil is over the allowed depletion limit level, then EAT will be constant as there will be no water deficiency (Senay, Verdin& Rowland, 2011). However, whenever the water level in the soil is lower as compared to the allowable depletion level, then WR will be higher than EAT regarding the remaining soil water (Molly & Chourlarton, 2008). Moreover, when the maximally allowable level of depletion is exceeded, the crop is likely to run short of water, thereby experiencing a serious structural damage through wilting and limiting its capability of producing enough healthy grains, and hence reducing the crop yields (Verdin, 1999).

For this model to work out correctly, the soil water content is a significant aspect. It is determined through an equation of mass balance whereby an experiment is conducted by experts who monitor the soil water level (“Sensors | Free Full-Text | Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling | HTML,” n.d.). The soil water content highly depends on the water-holding capacity of the soil under investigation and the root depth of the plants used. According to FEWS NET, the most significant aspects that affect the WRSI model are the potential evapotranspiration and precipitation process in the soil (Smakhtin, Revenga & Döll, 2004). The calculation of WRSI needs a start-of-season (SOS) and end-of-season (EOS) value in every modelling. The maps of SOS and EOS are used to determine the spatial variation of growing season timing (“NDVI-crop monitoring and early yield assessment of Burkina Faso – International Journal of Remote Sensing – Volume 14, Issue 8,” n.d.). They also determine the crop coefficient function that defines the pattern of crop water usage in crops. For instance, the water use pattern for maize and sorghum differs making them have different WRSI equations (Ventrella, Castellini, Giglio, Giacomo& Lopez, 2009).

The WRSI model has the capability of simulating different kinds of crops whose seasonal patterns of water use are published in terms of crop coefficient. At the end of every crop development cycle, the total water requirement (WR) and actual evapotranspiration (EAT) are used in the determination of WRSI in a particular Geographical Information System (GIS) at a spatial resolution of 0.1̊. When the value of the WRSI is 100, it means that there is no deficit. There is no yield reduction experienced as a result of water deficit (Acutis, Rinaldi, Mattia, & Perego, n.d). This means that in case there was a change in the yield, it was caused by other factors like physical damage, pest infestation, and lack of nutrients among others other than WRSI. All these issues are not usually catered for by the WRSI and has minimal effect to yield compared to water stress. In circumstances where the seasonal WRSI value is lower than 50 it is termed as a condition of crop failure (Verdin & Klaver, 2002).

In any given economy, yield reduction estimates in terms of WRSI in most cases contribute to food security planning in that nation. During the growing season, this indicator of crop performance can be evaluated after a given period as a monitoring tool (“Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model – Volume 21, Issue 18,” n.d.). As a warning tool, there is a need for EOS crop performance to be predicted using long-term data. The differences in growing seasons are the main reason for the presence of varying WRSI maps per regions (Nieuwenhuis & Boogaard, 2006). For example, Northern Africa and Southern Africa have different WRSI due to variance in the growing season (“Quantification of crop yields under rain fed conditions using a simple soil water balance model – Springer,” n.d.).

In most regions, a slight reduction in the WRSI is usually associated with large volume reduction in the crop yields. Sometimes a difference is experienced between the estimated yield reduction function and the reported one from the field (Ullah, Hafeez, Sixsmith & Faux, 2009). Now that the Values of WRSI for a certain crop highly depend on the duration of the growing season, which has an impact on rainfall distribution pattern, it has been recommended for individuals to use relative WRSI as opposed to Absolute one (Brown, 2014). This will ensure that the predicted estimates will at least predict the future trends in production thereby allowing an economy to prepare fully for any food shortage (“ARV Methodology – arc,” n.d.). In most parts, the forecast has been associated with water stress instead of yield forecast. This is because water-stress forecast is more applicable in that it can be further used to predict the yield levels of a given season (Smakhtin, Revenga & Döll, 2004). Moreover, WRSI is a very important aspect that aids most countries in irrigation planning whenever a shortage in soil water content is predicted (Woli, Jones, & Ingram, 2009).

It is evident that WRSI has a great impact on the crop yields of different parts of the world. As WRSI ranges from 0-100, a WRSI that is close to 100 in a given crop growing season indicates that the yields are likely to be maximized. Also, a WRSI that is below 50 in any given crop growing season will indicate that the season will be experiencing crop failure meaning that the crop yields will be very low (Nieuwenhuis & Boogaard, 2006). In other words, in a given crop growing season, the lower the WRSI, the lower the yields And the Higher the WRSI value the Higher the volume of yields to be harvested in that given crop growing season. In most cases, the WRSI estimates have proved to correspond with the real yields from the field in various seasons. Therefore, WRSI is a reliable indicator of crop yield levels of various seasons and has a great impact on cereals (Woli, Jones, & Ingram, 2009).

References

Acutis, M., Rinaldi, M., Mattia, F., & Perego, A. (0). Integration of a Crop Simulation Model and Remote Sensing Information. doi:10.1007/978-3-642-01132-0_34

ARV Methodology – arc. (n.d.). Retrieved from http://www.africanriskcapacity.org/africa-risk-view/methodology

Brown, M. E. (2014). Food security, food prices, and climate variability.

Estimating crop yields and production by integrating the FAO Crop Specific Water Balance model with real-time satellite data and ground-based ancillary data – International Journal of Remote Sensing – Volume 21, Issue 18. (n.d.). Retrieved from http://www.tandfonline.com/doi/abs/10.1080/014311600750037516

Martin, R. V., Washington, R., & Downing, T. E. (2000). Seasonal Maize Forecasting for South Africa and Zimbabwe Derived from an Agroclimatological Model. Journal of Applied Meteorology. doi:10.1175/1520-0450(2000)039<1473:SMFFSA>2.0.CO;2

Moeletsi, & ME, W. (2012). Water satisfaction analysis for dryland maize production in Frankfort. SASAS.

Molly, E. B., & Chourlarton, R. (2008). The famine early warning systems and remote sensing data. Berlin: Springer Verlag.

NDVI—crop monitoring and early yield assessment of Burkina Faso – International Journal of Remote Sensing – Volume 14, Issue 8. (n.d.). Retrieved from http://www.tandfonline.com/doi/abs/10.1080/01431169308953983

Nieuwenhuis, G. J., W, W. D., G, K. V., A, D. V., & Boogaard, H. L. (2006). Monitoring crop growth conditions using the global water satisfaction index and remote sensing.

Quantification of crop yields under rainfed conditions using a simple soil water balance model – Springer. (n.d.). Retrieved from http://link.springer.com/article/10.1007%2FBF00866391#page-1

Senay, G. B., & Verdin, J. (0). EVALUATING THE PERFORMANCE OF A CROP WATER BALANCE MODEL IN ESTIMATING REGIONAL CROP PRODUCTION.

Senay, G. B., Verdin, J. P., & Rowland, J. (2011). Developing an operational rangeland water requirement satisfaction index. International Journal of Remote Sensing. doi:10.1080/01431161.2010.516028

Sensors | Free Full-Text | Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling | HTML. (n.d.). Retrieved from http://www.mdpi.com/1424-8220/7/12/3209/htm

Smakhtin, V., Revenga, C., & Döll, P. (2004). A Pilot Global Assessment of Environmental Water Requirements and Scarcity. Water International. doi:10.1080/02508060408691785

Ullah, M. K., Hafeez, M. M., Sixsmith, J., & Faux, R. (2009). Integration of Remote Sensing Derived Actual Evapotranspiration with Nodal Network Water Balance Model in a Demand Driven Irrigation System.

Ventrella, D., Castellini, M., Giglio, L., Giacomo, E. D., & Lopez, R. (2009). Simulations of soil water balance in an irrigated district of Southern Italy.

Verdin, J., & Klaver, R. (2002). Grid-cell-based crop water accounting for the famine early warning system. Hydrological Processes. doi:10.1002/hyp.1025

Verdin, J. P. (1999). Geospatial climate monitoring products: Tools for food security assessment.

Woli, P., Jones, J. W., & Ingram, K. T. (2009). An Agricultural Reference Index for Drought.

WRSI (Radio station : Northampton, & Mass.). (2000). The riversound cafe: Volume 1. Northampton, MA: 93.9 The River, WRSI.