We exclude the potential of CIMIS values on non-drip acreage, noting that our estimates would therefore be conservative in that sense.The monetary cost of water saved can be viewed as savings on the intensive margin. One can also consider gains on an extensive margin. The water saved by use of CIMIS is likely to be used in agriculture as well. This means more acres can be grown with the same initial amount of water. The “full” economic value of the water saved by CIMIS in agriculture is the value of agricultural output that can be produced with it on acres not irrigated before. This following analysis includes the economic value of growing alone, without the added values of post-harvest and economic multiplier effects, and probably a safe lower bound. We do not, however, include a counter-factual productivity of non-irrigated land. In California, this is probably range land or acreage that is too sloped for traditional irrigation methods, and therefore of very low economic productivity. With 1.92 million AF of water saved by CIMIS, and an average use of 2.5 AF/acre by growers , the savings from CIMIS can water an extra 768,000 acres in California. To put this in context, this is about double the total walnut acreage in 2016. Because of economic and technical constraints of water transport, it is hard to determine which crops would be planted in these extra acres. A conservative approximation assumes that the water saved by CIMIS serves to replicate the existing distribution of crops ,drainage gutter taking the average value of productivity of an acre as the benchmark. The weighted average of grower revenue per acre in 2016 was $3,757 per acre1 .

Multiplying by 768,000 acres, a conservative approximation for the contribution from CIMIS to California’s GDP via agriculture is about $2.89 billion. These are gains from water saving in parks, golf courses, and gardens. They were assessed as a small portion of the total gains from CIMIS in the 1996 report by Parker et al., totaling about $2.3 million . Our current estimate for these gains is much higher. The discrepancy from the 1996 report is due to several factors. First, we believe to have reached out to more respondents in this sector. Second, water prices in California have gone up substantially. Third, there might be more use of CIMIS and smart irrigation planning in the sector compared to 20 years ago. We focus on responses from landscape managers and golf course managers. They report their operating acreage , the average water use, and the estimated saving rate by using CIMIS. We have 28 respondents in golf courses with 6,750 acres in total, and 137 respondents in landscape management with 179,000 acres. The total sum is about 21 times the acreage of the equivalent category in the 1996 report. Based on the initial interviews, we grouped them into a single user category, but still asked them to select into landscape or golf later in the survey. Table 2.2 proved us wrong. Surprisingly, it turned out that the users in landscape management reported much higher water saving rates with CIMIS. This could potentially be explained by technology: big turf areas are still likely to be irrigated with sprinklers, which allow lower savings rates even if CIMIS is used for optimal water calculations. On the other hand, a lot of non-turf landscaping might be irrigated with drip. The total amount of water, saved yearly with CIMIS according to our respondents, is 220,707 AF. Water prices for these types of users are much higher than in agriculture. We can use the municipal water rates to get an estimate of the monetary savings. The EBMUD rates, effective 2018, are $5.29 per 100 cubic feet or $4.12 for non-potable water.

The Los Angeles Department of Water and Power charges commercial, industrial and governmental users by tiers. For January 2019, the tier 1 rates are $5.264 per 100CF, and tier 2 rates are $8.667. The specific tier 1 allotment is set for each user. However, some non-profit users might get rates as low as $2.095 for tier 1 and $3.595 for tier 2. For comparison with agriculture, note that the lowest rate cited above for municipal water is more than four times higher than the “high” rate for agriculture in Taylor, Parker, and Zilberman . The spread of prices, even within municipalities, suggests that they might not reflect the marginal cost of providing water to consumers. However, water utilities have regulated rates and usually work on a “cost plus” basis, such that the water rates should reflect their real average cost. These rates can therefore be used to assess the economic gains from water savings. The different municipal rates serve to construct bounds for our estimates. This first order approximation does not take into account the potential elasticity in water demand, or the potential effect of CIMIS in lowering residential water pricing by curbing down demand. However, we think they are good benchmarks and could definitely serve as an estimate for order of magnitude. The lower rate is the LADWP non-profit rate, which might not apply for many CIMIS users. Assuming nobody exceeds their tier 1 allocation, the value of water savings amounts to $201 million per year. For a higher EBMUD rate of $5.29, the savings amount to $509 million per year. For a reasonable upper bound, assuming we are in Los Angeles and 90% of the water consumption is in tier 1 , the sum is $539 million. Unlike the case of agriculture, we do not believe the survey responses in this category have captured all the relevant acreage. Neither do we have a good sense of the total relevant acreage in California, which could indicate by what factor these estimated gains could be extrapolated.

However, the sums are substantial as they are. We take them as our total estimates for gains from CIMIS, noting that they are an under-estimate in this Sense.This chapter analyzes the gains from CIMIS, focusing on agriculture and some urban uses. The gains are much higher than the ones found in the 1996 report. This is partly due to increased economic activity in general, but probably has to do with more adoption of smart irrigation as well. The total yearly gains in agriculture range between $492 million, taking only the intensive margin effects, and up to about $1,982 million considering the extra acres that can be grown with the saved water. A surprisingly large sector using CIMIS is landscaping and golf courses, with yearly monetary savings of at least $201 million for our survey sample alone. Several other user types were included in the survey, indicating a substantial role of CIMIS in areas crucial for California’s economy. Respondents use CIMIS to plan drainage in agricultural and urban settings, taking advantage of CIMIS historic rainfall records. CIMIS is used for water budgeting and even pricing. Researchers in the public and private sector use CIMIS for diverse purposes,large square pots from basic research to calibration and verification of other weather related products. These are just a few of many additional uses of CIMIS we know about, but do not quantify here due to the complicated methodological framework required. The economic gains from CIMIS surely surpass the ongoing costs of a system with less than a dozen employees. However, could these gains be achieved by the private sector? The decreasing costs of weather sensors mean that growers and other users could potentially access precise data on their own. If we wanted a cheap weather station, costing about $1,000, for every 1,000 acres of drip irrigated land in California, the total cost would surpass$2.8 million, plus some ongoing costs for maintenance. This, however, would prevent many benefits from the centralized aggregation of data and the historical records that are crucial for research and planning, as one could not assure that aggregation of the data from all these separate private stations would occur. While several online aggregators of weather information exist, many rely on the public information provided by networks such as CIMIS and other government bodies such as airports and air quality monitoring systems. It is not obvious that private aggregators would be profitable if they had to purchase this information, or what their WTP would be. Moreover, the ET measurements which many growers use are usually not captured by commercial stations, and there are concerns regarding the reliability of ET approximations by other variables. The development of satellite technology might change these conditions in the future.This chapter deals with the non-linear effects of temperature on yields in California pistachios . Dealing with a perennial crop presents challenges rarely encountered in research on crops such as corn, soybeans, and rice. A first challenge has to do with biology. Annuals are grown from seed every year. Each yield observation is treated as an independent draw from a distribution.

Some adjustments for spatial correlation might be taken, but the interactions of consecutive yields, e.g. via pest build-up or changes in soil chemistry, are mostly overlooked.1 Perennials, on the other hand, do experience yield effects of factors such as tree vintage, carry-over from past years, and alternate bearing patterns. These processes are responsible for some of the factors obfuscating the real relationships between temperatures and nut yields . Altogether, the potential for statistical noise, stemming from correlations between error terms, is much higher in perennials than annuals. A second challenge is that temperature might affect yields not only in the growing season and not only via the common implicit “heat stress” mechanism. For perennials, temperature effects might be greatest before or after fruit bearing time. This chapter deals with the effects of temperature on pistachios during their winter dormancy phase. The following brief explanation of dormancy is based on Erez . Many fruit and nut trees, including pistachios, have a dormancy phase during winter. This phase is an evolutionary adaptation, allowing the tree to “hibernate” and protect sensitive organs while harsh weather conditions take place. Trees prepare for dormancy by storing energy reserves, shedding leaves, and developing organs to protect the tree buds. Once a tree went into dormancy, it needs to calculate when to optimally “wake up”. Blooming too early might expose the foliage to frost. Blooming too late means not taking advantage of available resources , and eventually being out-competed.These signals involve day length and temperatures. Failure to attain threshold signal levels, varying between crops and varieties, leads to late, low, and non-uniform bud breaking, which is linked to low yields at harvest. This threshold mechanism means that small changes in the temperature distribution can have large effects on yields, especially in the warmer areas where the chances of not reaching the threshold signal are higher. Several agronomic models exist for this dormancy exit mechanism and the role of temperature in it. The Dynamic Model seems to be the most precise in predicting bloom in many temperate areas such as California . This model uses a metric of chill portions , which are calculated with a vector of hourly temperatures. The formula is sequential, mimicking chemical dynamics which depend on the concentrations of substrate and product. Chill portion build up depends on these concentrations and the ambient temperature. Roughly speaking, when temperatures go above 6oC, accumulation slows down. When temperatures exceed 15oC, the process reverses, and the CP count quickly drops to the last integer portion that has been “banked”. Thus, rising winter daytime temperatures can have a detrimental effect on chill count, even if the temperatures themselves are not extreme on the yearly distribution, because they interfere with the build-up of chill portions. This mechanism is an example of the complex modeling issues, from the biology perspective, when dealing with perennials: it require crop specific agronomic knowledge, and the CP build up model makes it impossible to assess the marginal effects of certain temperatures, as CP are not linear in degree hours. A third challenge with some perennial crops is the limited information on yields. Heterogeneity in local weather conditions increases the statistical power of traditional yield panels in annuals, with acreage spreading over many geographic regions.