The climate prediction data produce a point estimate of chill portions for each year in 2020-2040. For a given set of model parameters and climate predictions for 2020-2040, the model is solved numerically twice for each year in this range. The consumer, grower, and welfare gains are calculated for each year using these two simulations. Using a discount rate of 5%, I can calculate the Net Present Value of the MCE gains in 2019. For each scenario, I run this procedure for 100 “independent draws” of 2020-2040 prediction paths. For each one, an entire simulation is run to produce an NPV of the gains. I report the Expected NPV , the mean of this distribution, and standard errors around it. More details on the numerical solution of the model can be found in appendix A.3.Before I present the simulated welfare gains, there is one more piece in the puzzle. The calibrated model is set with 2016 acreage . Pistachio acreage through 2020- 2040 is likely to be different, and most likely higher than that. However, the model does not include endogenous growth of planted and harvested pistachio acres. To give some bounds on the expected gains, I run the simulations with four different acreage growth scenarios, each specifying a different pistachio acreage growth path until 2040. All scenarios assume some growth path until 2030, when acreage stabilizes and stays fixed through 2040. The first scenario is “No Growth”, meaning that 2020-2040 climate predictions are cast over the 2016 acreage. This should give a lower bound for gains, as acreage is predicted to grow and not shrink. The second scenario is “Low Growth”, which sets the yearly growth of harvested acres until the year 2022 at 9.6%, the average rate since 2000,macetas de plastico 30 litros and then sets zero growth . The growth until 2022 is attributed to currently planted but not yet bearing acres.
This assumes that we are on the brink of a dynamic equilibrium in growth, and therefore no new acres will be planted in California. This scenario should give estimates that are higher than the “No Growth” scenario, but still rather conservative. The third scenario is “High Growth”. This one sets the growth rate until 2022 at 14.6%, the average rate since 2010, and then lets pistachio acreage follow the historic path of almonds in California . That is, the growth rate of almonds when they had the corresponding pistachio acreage. This very optimistic growth prediction makes the “High Growth” scenario the upper bound for the gains from MCE. One potential concern with acreage growth is that growers might switch new acreage to unaffected counties, or plant more heat tolerant varieties. For this, the “High North” scenario takes the high growth rate, but all new acreage harvested from 2023 is located in an imaginary “North” county, where chill damages are virtually zero. Note that planting in the unaffected north has the same effect on supply as planting a more heat tolerant variety near the existing locations . This last scenario is, in my opinion, the most plausible in terms of MCE gain magnitudes. A summary of the growth rates is depicted in Figure 4.2. In all scenarios, demand grows by the total rate of acreage growth. MCE could help overcome a climate challenge for California pistachios. I model the market and assess the potential welfare gains from a reflective coating technology that lowers the effective temperatures in pistachio orchards. The expected NPV in 2019, for the gains from this technology between 2020 and 2040, is predicted to be around $2.7-3.5 billion. These come from consumer surplus gains, as the total gains for growers in the main specifications are negative. The latter result is not unheard of in agricultural settings, where a negative supply shock can actually increase grower profits. For example, Carter et al. show that the 1979 labor strikes in California actually increased revenues and profits for lettuce growers.
The simulation results shows the flip side of the coin: solving a supply shock can lower grower profits. While less tangible than actual registered profits, consumer surplus gains are real economic gains enjoyed by the public. This point holds even when discussing a narrower welfare framework for California alone. Part of the modeled gains in consumer surplus are enjoyed elsewhere, as the majority of pistachio output is currently exported. However, export demand is usually considered more elastic than domestic demand, making the share of local consumer surplus gains disproportionate to the share of local consumption. At a share of 1/3 of total consumption, let us assume that Californians still enjoy half of the consumer surplus gains from MCE . Adjusting Table 4.1, the total welfare gains in California are strictly negative when the demand is unrealistically inelastic, εD = −0.5, and strictly positive for more realistic demand assumptions . The scope of consumer surplus gains brings us to the potential gains from public investment in R&D for MCE solutions. With social returns from investments largely exceeding private ones, this type of research is a good candidate for prioritizing in public research fund allocation . The case for public research is made stronger by the fact that there seems to be little private incentive to invest in MCE, at least in this case. I see MCE technologies mostly as an adaptation of existing ones to solve a climate problem. Therefore, innovations in the field would be hard to make proprietary by the innovator. Moreover, innovators are likely to come from the industry: a large growing firm would have the resources and access to enough pistachio acreage to run experiments and develop new MCE solutions. But if this firm sees that a world with MCE is worse, why invest in innovation? Adding market power to the equation makes an even stronger potential case for public R&D: the total welfare gains are higher, and the incentives for innovation could be even lower.
One could imagine, with further agronomic research, other MCE technologies applied to other fruit and nut crops, and even for annuals such as corn or soybeans. Of course, these are less profitable than pistachios, but they face similar challenges, and MCE solutions are not necessarily very expensive. Other implications could be with the distribution of climate change damage incidence. Technologies might only be available to growers in countries better off financially, further exacerbating international income disparities. An interesting potential for MCE technologies could be in accelerating the transition of agricultural practices closer to the poles, sometimes referred to as the “crop migration” . For example, MCE solutions for frost could accelerate the expansion of viticulture to higher latitudes. The simulation based valuation methodology in this chapter has its caveats. Modeling supply and demand as linear is obviously a simplification. The assumptions on growth and distribution of acreage are based on past growth patterns, and might not reflect unexpected future changes in market conditions. The future chill predictions are in line with other predictions by climatologists, yet might fail to materialize. Nevertheless, by choosing various scenarios, basing the parameter ranges in the literature, and choosing conservatively when possible, I believe to have gotten a reasonable range for the potential gains from MCE in California pistachios. They are in the low billions for a crop of secondary importance to California agriculture. I believe this shows a great potential of MCE technologies for climate change adaptation in general.South Asian agriculture is a global ‘hotspot’ for contemporary and future climate vulnerability. Further, 1.7 billion people live in South Asia,cultivo hidroponico and by 2050, that number is expected to rise to 2.4 billion. Although the region enjoys high economic growth, it suffers from extreme poverty, undernourishment and the deterioration of natural resources1 . South Asia has more than 42% of the world’s poor , about 21% of the population is undernourished, and more than 41% of children are underweight2 . Rapid population growth will increase the demand for cereals by about 43% between 2010 and 2050. Meeting this projected need is doubly challenging considering 94% of the land suitable for farming is already in production and 58% of agricultural areas face multiple climatic hazards such as water shortage and extreme heat stress. The present situation is anticipated to worsen with climate change, with rising temperatures and changing monsoon rainfall patterns projected to cost India 2.8% of gross domestic product. Although global crop productivity has more than doubled during the past decades, negative impacts on environment, biodiversity, soil quality and air quality are common. Future food production in South Asia requires new management approaches that are efficient and climate smart to make tangible contributions to the United Nations’ Sustainable Development Goals . Conservation agriculture has emerged as an alternative to an inefficient tillage-based conventional agriculture. CA is an ecosystem approach to regenerative sustainable agriculture and land management based on three interlinked principles: continuous no or minimum mechanical soil disturbance, permanent maintenance of soil mulch and diversification of cropping system , along with other complementary good agricultural production and land management practices. CA helps in managing agroecosystems for improved and sustained productivity, increased profits and food security while preserving and enhancing the resource base and the environment.
It is estimated that a partial CA-based system is spread to over 2.5 millionha in South Asia . Numerous favourable impacts have been reported in the global literature on CA, including for crop yields, resource use efficiencies, timeliness of cropping practices, soil quality and ecosystem services. Nevertheless, a meta-analysis of global yield data from 48 crops across 63 countries reported limited yield gains with full CA or with some components of CA14, a result that has drawn into question the wisdom of making CA a sustainable intensification priority for agricultural development programs. Although the benefits derived from CA have been broadly questioned, there has been gradual increase in adoption of CA over time. Zero-tillage wheat has been adopted on a significant area in the rice–wheat system of the northwestern Indo-Gangetic Plains and in the Eastern Gangetic Plain with positive impacts on wheat yield, profitability and resource-use efficiencies. The national governments in South Asia are actively promoting CA to address sustainability problems. Although numerous on-station and on-farm studies have been carried out during the past two decades to evaluate CA in South Asia, a systematic synthesis of evidence is lacking. To clarify the regional potential of CA as a full package or combination of its components in South Asia, this study presents a comprehensive meta-analysis on data from on-station and on-farm studies in South Asia’s dominant cereal-based cropping systems. Performance parameters considered in the analyses included grain yield, protein-equivalent yield , water use efficiency, cost of cultivation and net economic return, and emission of GHGs and global warming potential . Results are contrasted with conventional best practices and contextualized with respect to potential contributions to the SDGs related to poverty, hunger, health, climate action and clean water.The first-stage analysis showed improvements in all the measured performance indicators . Compared with conventional practice, CA had 4.6% higher grain yield, which was very similar to PEY. A 14.6% increase in water use efficiency was observed with CA. The net economic return increased by 25.6% . Segregating on-station and on-farm studies revealed higher CA responses in the former than in the latter. On-station crop yields increased by 11.1% and on-farm by 4.7%, while water use efficiency was 29.3% and 9.3% higher in the on-station and on-farm studies, respectively. However, the changes in the economic return in on station and on-farm studies were similar. Analysis based on cropping system revealed that the maize– wheat system had the highest grain yield increase with CA , followed by rice–wheat and rice–maize . The ‘others’ category also demonstrated improvement in grain yields . Similar trends were obtained in on-farm studies. In on-station studies, however, all cropping systems showed no change except maize–wheat, which had a 5.8% higher yield with CA . The PEY followed a similar trend as in grain yield . Water use efficiency with CA improved by 28.5% in the maize–wheat system, which was higher than in rice–wheat , rice–maize and other crop systems . Water use efficiency in rice–wheat was higher in the on-station studies compared with the on-farm trials, while the rest of the cropping systems could not be compared between on-station studies and on-farm trials due to nonavailability of data in one or the other .