Multi‐objective crop planning using pareto‐based evolutionary algorithms

The scarcity of water is a growing problem worldwide. The increasing use of water in industrial, urban, and agricultural applications together with the continuous increase in population require the proposal of efficient solutions. In the case of agricultural use, it is necessary to not only maximize the economic benefits, but also to establish optimal water‐saving crop planning, especially for water‐deficient regions. Due to the multi‐objective nature of these problems, the decision‐making process is complex. Fortunately, the increase in computational resources available in recent years has allowed researchers to develop efficient computational algorithms to deal with real and complex optimization problems, including agricultural ones. In particular, multi‐objective evolutionary algorithms (MOEAs) are known for their ability to optimize several objective functions simultaneously to provide a representative set of the Pareto front, which is a set of problem solutions representing a trade‐off between the best values of each of the objectives. This article proposes solving a multi‐objective crop planning problem using two Pareto‐based MOEAs. Results obtained when solving this problem using real data collected from a large number of greenhouses in Spain to show the advantages of using these multi‐objective approaches.     

Targeting the biotechnology clusters in North Carolina and Israel: lessons from successful and unsuccessful policy making

This paper analyses the development of biotechnology clusters in North Carolina (NC) and Israel. In both NC and Israel, when the biotechnology was identified as a potential strategic priority, the framework conditions were suitable for successful policy-targeting. NC presents a case of a successful transition from a traditional manufacturing economy to a knowledge economy. The most successful part of this transition was a policy-led development of a biotechnology cluster in the Research Triangle. While Israel also presents a case of a successful transition from a low-tech economy to a knowledge-intensive economy, Israel failed to develop a successful biotechnology cluster. We suggest that this failure is mostly due to a failure to implement policy to encourage such development. We argue that the elements, which separate NC's success from Israel's failure, are: a clear vision and strategic planning; timely response, long-term commitment; strong leadership; cooperation between the government, private sector and academia, and an adjustable policy-making process.     

Evolutionary targeting

Evolutionary Targeting is a dynamic, systems-evolutionary policy perspective which focuses on triggering, re-enforcing and sustaining market-led evolutionary processes of emergence of Multiagent Structures (industries, clusters, markets, etc). A major aspect is leveraging existing successes in firms to promote emergence of such structures. This requires discrete policy interventions directed at varying areas of system/market failure, which make their appearance at difference phases of the overall process. The paper briefly illustrates the approach through an analysis of VC policies in Israel and selected European countries, and by referring to the traditional view of Infant Industry development and existing views on high tech cluster development. The resulting framework of analysis, which differs radically from the ‘Picking Winners’ policies of the past and from the successful targeting of infant industries in Korea and post war Japan, seems to fit the increasingly turbulent and high return/high risk global environment prevailing today.

State contract law and the use of accounting information in debt contracts

Review of Accounting Studies - This paper examines the relation between state contract law and the use of accounting information in debt contracts. Contract theory suggests that balance sheet based...     

Measuring technical efficiency of Spanish pig farming: Quantile stochastic frontier approach

The pig meat production plays a significant role in the Spanish agrofood system. The assessment of the efficiency performance with which farmers are operating is necessary to define adequate policy and management strategies. In this context, this study aimed to determine the technical efficiency (TE) performance of pig farms and to examine the key factors that may affect the production system in Spain. To do so, the analysis relies on the quantile stochastic frontier model using a sample of Spanish pig farms. Results show a significant difference between production frontier parameters across the selected quantiles, which support the relevance of using the quantile regression approach. The optimal quantile for the stochastic frontier indicates an average TE level of 75%. In addition, empirical findings suggest that pig farmers in Spain give more importance to the adoption of high technology to improve their economic and technical performance as well as their competitiveness at the European pig market.