Econometric approaches illustrate that California agricultural land value may be particularly vulnerable to changes in surface water supply and nonlinear temperature effects . Deschenes and Kolstad also illustrate that farm profits may be more responsive to climate than annual fluctuations in temperature and precipitation. Several analyses illuminate our understanding of adaptive capacity. The overarching focus for many CALVIN-SWAP studies is to start with a worst-case scenario approach and see how well we fare even with some of the best-case farmer and institutional responses . Joyce et al. also illustrate an example of adaptive capacity through time. Assuming drip irrigation is more widely adopted in the Central Valley by mid-century, they find that groundwater pumping declines. However, as the climate continues to warm towards the end of the century, the positive effects of drip irrigation are eliminated. Beyond this, a discussion of adaptive capacity is lacking. We have moved ahead in the past 15–20 years from the early agro-economic assessments of the early/mid-1990s, but it appears that we are also standing still. This review has illustrated the various ways comparative static approaches have incorporated adaptive actions to illuminate our understanding of climatic impacts to California agriculture. But, as critics suggest ,hydroponic barley fodder system questions of when and how much farmers and institutions will adapt are left unanswered.

Responsiveness — the key characteristic of decision-making — is only vaguely addressed, and, important distributional consequences of climate impacts to agriculture while alluded to, are not identified. Lack of responsiveness and distributional consequences is mostly due to a dearth of individual farm-level data, rather than the incapacity of programming and econometric approaches to accommodate a more specific analysis. Using the same county-level data with more innovations in a comparative static framework could only take programming and econometric approaches so far. There is also a degree of comfort with identifying the primary barrier to moving forward as uncertainty in climate projections. While vulnerability arises out of biophysical processes, it is critical to understand that it is imposed on a pre-existing, dynamic socioeconomic structure . It is important that our economic models do more to capture this structure.Over the past decade, the structure of the plant breeding and agricultural biotechnology industries has been radically transformed. Through dozens of mergers, acquisitions and strategic alliances, there has been a rapid and dramatic concentration of control over value-generating assets, particularly intellectual property. This restructuring follows a significant strengthening of the intellectual property rights of plant breeders due to the Supreme Court ruling Diamond v. Chakrabarty, and advances in biological sciences. Over the same time period, there has been a broader pro-patent movement in American industry . We explore the relationships among scientific innovation, intellectual property rights, and industrial restructuring in plant agriculture. Figure 1 presents the frequency of corporate mergers and acquisitions in agricultural biotechnology from January, 1984, through April, 2000.

The purposes served by consolidation could also be served by vertical integration and/or contracting. Why, then, has the frequency of organizational consolidations been so high in recent years in agricultural biotechnology? One concern is that this trend may reflect a relentless quest for market power. Other, less sinister motivations for consolidation include the mitigation of contractual hazards and the exploitation of asset complementarities. Our specific concern regards the role, if any, that intellectual property has played in the restructuring of the U.S. agricultural biotechnology industry. One obvious question that arises is whether the evolution of patent 9wnership mirrors the changing structure of the industry. As shown in Figure 2, the concentration of agricultural patent holding fell from the mid-1980s through the mid-1990s. There is a trough in the mid-1990s, and since that time the concentration of patent holdings has risen, consistent with the merger and acquisition frequencies in Figure 1. Patent data provides an accurate picture of the consolidation of intellectual property rights, since patent data is publicly available for both public and private firms. We develop a theoretical explanation of the reorganization of firms and the consolidation of intellectual property rights that is based on the transition of agriculture from a commodity system toward a differentiated product system and the development of improved intellectual property rights in agriculture. We demonstrate that the combination of these two factors can explain the consolidation in firm ownership and intellectual property observed in Figures 1 and 2. However, neither factor alone is sufficient to influence the incentives to integrate, nor the choice of integration method. The goal of our analysis is to determine whether the nature of the product system and the intellectual property regime influence the incentives for vertical integration or the choice of integration form. We consider a two stage production chain: the first stage is trait development, which provides an input for the second stage, seed production. We assume that there are two seed producers, and at least two trait developers.

Seed producers may sell either a basic seed, which has a perfectly elastic demand function, or an augmented seed containing a trait developed by the trait developer, which has a downward-sloping demand function. We model two product systems: a commodity system, where the demand for the augmented seed is invariant to the actions of the seed producer, and a differentiated product system, where the demand for the augmented seed is influenced by the actions of the seed producer. We model two intellectual property regimes: one in which intellectual property rights are clearly articulated, the IP-regime, and one in which they are not, the NIP-regime. 2 In order to evaluate the effects of these factors on the incentives to vertically integrate, we compare returns under non-exclusive licensing and under exclusive licensing. In order to evaluate the effects of these factors on the choice of the method of vertical integration, we compare returns under exclusive licensing and under consolidation through a merger or acquisition. In each product system, markets are open for two periods. In the first period, a single agent develops an R&D product called a trait, incurring a fixed cost and zero marginal cost. The developer holds a monopoly over his trait for that period, and can choose whether to license non-exclusively, license exclusively, or consolidate with a seed producer. What happens in the second period depends on the intellectual property rights regime. Under the IP-regime, the trait developer attains a patent and retains his monopolistic status in the second period. Under the NIP-regime, other suppliers will imitate the trait developer’s product in the second period, and price competition will drive the equilibrium price of licenses, and hence equilibrium profits for the innovating trait developer, to zero. In the integrated monopoly case, in contrast, the endogeneity of the output price partially offsets the negative effect on an increase in the slope of the inverse supply curve. A larger demand intercept increases the profits obtained in the integrated monopoly case, holding other parameters constant. The endogeneity of the output price enhances this effect,livestock fodder system relative to the effect in the non-exclusive license case. Period Two Equilibrium. The returns to non-exclusive licensing and the returns to vertical integation through exclusive licensing in period two are affected by the intellectual propery regime. In the IP-regime, the analysis of non-exclusive licensing in the second period will be exactly the same as in the analysis of the first period in the preceding subsection. In particular, the supplier will earn profits of ;;r: in each period, if he sells licenses on a non-exclusive basis. In the NIP-regime, other suppliers will imitate the trait developer’s product in the second period, and price competition will drive the equilibrium price of licenses to zero, since trait development is assumed to have only an initial fixed cost and zero variable costs. Second period imitators do not incur the initial fixed costs.

As an alternative to marketing its product nonexclusively, the trait developer can negotiate an exclusive arrangement with one of the seed providers. Regardless of the intellectual property regime, in the first period augmented seed will be monopolistically supplied in the final market. In the NIP-regime, other suppliers will imitate the trait developer’s product in the second period, so that an exclusive license can not be maintained. As in the case of non-exclusive licensing, the price of licenses, and trait developer profits, will be driven to zero. In the IP-regime, augmented seed will be monopolistically supplied in the final market under an exclusive license in both periods. Within a commodity system, the intellectual property regime does not affect the incentives to vertically integrate or the choice of integration form. Within a differentiated product system, the incentives to integrate are affected by the intellectual property regime. In the absence of intellectual property protection, the differentiated product system generates the same incentives to integrate as the commodity system does. Under intellectual property protection, the incentive to vertically integrate is increased relative to the case of no intellectual property rights, and relative to the commodity system case. Thus, the combination of increased intellectual property protection and increased product differentiation can explain the tendency toward increased integration in the agricultural seed market. However, as Proposition 4 states, these two factors can not explain the tendency of this integration to occur through consolidation, rather than exclusive licensing. In the following subsection, we introduce an explanation for this tendency.A differentiated product system imposes a multitude of demands on management that simply do not arise in a commodity system. In particular, to compete in a differentiated product system, firms’ managers must acquire, interpret and respond strategically to massive amounts of information regarding their customers, products and suppliers. All this takes time and effort, and intellectual property protection is required in order to justify the investment in both. Different management teams may tend to have widely divergent views about their abilities, to meet these challenges, relative to their perception of the abilities of their competitors. This suggests that our assumption of homogeneous beliefs may be overly restrictive. Party i may be unwilling to cede control over his component of the joint product to party j through a licensing agreement, because he lacks faith that j will be able to “capture the value” inherent in the joint relationship. If party j is to manage the production and marketing process, party i’s subjective expectation of its royalty revenues may be sufficiently pessimistic that no licensing agreement will be feasible. By 2050, the US population is estimated to grow to 400 million and the world population to 9.7 billion . Current agricultural practices account for 70% of global water use, energy use is one of the largest costs on a farm, and inefficient use of agrochemicals is altering Earth’s ecosystems . With finite arable land, water, and energy resources, ensuring food, energy, and water security will require new technologies to improve the efficiency of food production, create sustainable approaches to supply energy, and prevent water scarcity . Monitoring of agricultural crops is still accomplished primarily through the expensive, labor-intensive, and time-consuming process of crop scouting, by manual sampling and documenting the state of the field. Precision agriculture involves the use of technology to acquire and analyze data from the field. However currently technologies such as sensors are limited or non-existent to spatially, temporally, and compositionally monitor the state of the field, data is coarse-grained and siloed in equipment, communications infrastructure is limited or nonexistent on the farm, and interventions are reactive and over provisioned, increasing economic and environmental costs. While the concept of precision agriculture has existed for 30 years, the exponential growth in information technology and data science and the reduction in their cost is setting the stage for the next revolution in agricultural practices. The National Science Foundation Engineering Research Center for the Internet of Things for Precision Agriculture was established on September 1, 2020 and is a collaboration between faculty and students from the University of Pennsylvania, Purdue University, the University of California-Merced, and the University of Florida, with partners in education, government, industry, and the end-user farming community . The Center unites a convergence of expertise in agronomy, agricultural engineering and economics, and environmental science and in the science and engineering of physical and cyber systems.