Climate Variability and Agricultural Systems

Water Freedom System

Survive Global Water Shortages

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According to Oram (1989), agriculture represents one of the most weather dependent productive sectors. In addition to that, it is also the largest consumer of water resources due to the extensive surface that crops utilize during their development. Rosegrant et al. (2000) identify climate variability and the growing competition for water among economic sectors as two key issues that a modern society has to face when designing efficient water allocation policies.

Given the sensitivity of agricultural systems, in situations where timely and skillful climate forecasts are available, such information could be of great value as long as the system shows a response to the climatic signal and there are alternatives that can be targeted to the forecast resulting in different optimal strategies of water resources management.

One of the most simple but useful forecast system corresponds to the use of climatic signals. These can be identified, monitored, and used as a forecasting tool in order to estimate possible scenarios of weather sensitive systems. This is the case of El Niño-Southern Oscillation phenomenon which has been described as a factor that can explain an important fraction of climate variability in several parts of the world (Walker 1923; Ropelewski and Halper 1996). As other parts of the world, the climatic regime of central Chile is exposed to important fluctuations that, up to some extent, can be associated with El Niño phenomenon. In central Chile, changes in the precipitation regime have been studied and associated with the Southern Oscillation Index (SOI), which points to a tendency to observe anomalously dry conditions during the positive phase of the Southern Oscillation (La Niña phase) (Rubin 1955; Pittock 1980a). In addition to that, precipitation is likely to be abundant during the Niño years, corresponding to the negative phase of the SOI (Quinn and Neal 1982).

Temperature changes have also been studied by Pittock (1980b) stating that there are warm temperature anomalies in conditions where sea surface temperatures are above the mean (El Niño years). Rosenblüth et al. (1997) showed that there is a negative correlation between mean temperatures and the Southern Oscillation Index with a tendency to be warmer during the negative phase of SOI (corresponding to El Niño years) and colder when La Niña is present (positive phase of SOI).

Daily meteorological variables have also been studied conditioned on El Niño phases. Maximum, minimum and dew point temperatures as well as wind speed were analyzed by Meza et al. (2003). They concluded that the influence of El Niño phenomenon is not as marked as in the case of precipitation. However, the precipitation regime does affect other meteorological variables because there are differences between days with and without precipitation. It was later demonstrated by Meza (2005) that El Niño does have an influence on reference evapotranspiration in central Chile becoming a phenomenon that would represent an important tool for water resources management. It was found that agricultural water demands can be up to 20% higher during La Niña years.

Several studies show how climatic information derived from ENSO forecasts can be used to accurately estimate yield outcomes and crop water demands. Yield responses as a function of ENSO phases are illustrated for several crops and agricultural systems (Phillips et al. 1998; Podestá et al. 1999). It has been also shown that the use of climate forecasts can bring additional economic benefits because the decision-maker can target specific management strategies to the future forecasted events (i.e. there is an economic value in climate forecasts). Examples of such work are found in Adams et al. (1995), Messina et al. (1999), and Hammer et al. (2001). For central Chile the value of ENSO-driven climate forecast has been estimated for perfect and imperfect knowledge of future El Niño phases for different crops and agricultural systems (Meza et al. 2003).

A straight forward way to analyze the response of crops to climatic variability throughout water use (and therefore irrigation) can be done by looking at the water use efficiency factor, defined as biomass generated per unit of water transpired. To represent this situation, Doorenbos and Kassam (1979) define a Ky coefficient, which is known as yield response to water factor. The general equation proposed is:

Here, Ya is the actual yield of the crop (kg ha Ym corresponds to the maximum yield (kg ha-1), ETa is the actual crop evapotranspiration (mm), and ETc the potential crop evapotranspiration (mm). A Ky value less than one means that the crop shows less sensitivity to water restrictions, whereas a Ky value higher than one implies that the crop is highly susceptive to water stress. Even though it is a useful relationship for irrigation planning, there are some ambiguities in applying this method for optimum water allocation. The authors do not consider successive and different levels of water stress which may occur in reality. It is not clear whether the method has to be applied in multiplicative form (i.e. the effect of the stress in one stage is carried to the following stage) or taking the minimum value of all stress stages.

Jensen (1968) proposed a mathematical relationship that is easier to apply and considers the effects of individual non equal levels of water stress over crop yield. The Jensen model is:

where X{ is the stress sensitivity index for each developmental stage i (i = 1,..., N). For each crop i = 1 corresponds to the vegetative phase, i = 2 is the flowering phase, i = 3 is the fruit development phase, and i = 4 represents the harvest phase. The variable j represents the number of days in each phase i.

Note that, under drought conditions, decisions made on early stages regarding to the amount of water allocated may affect final yield, either by restricting the rate of actual evapotranspiration in the current period and/or affecting the future ones because they determine the soil water content that will be available in subsequent periods. For this reason information regarding future water demands (ETc) may be useful to define an optimum irrigation management.

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