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Teosinte is an invasive weed which emerged recently in Northeastern Spain, an important corn-growing region in Western Europe. It is causing substantial agronomic and economic damages and is threatening the availability of corn in the region. Farmers and regulatory agencies can choose from a number of strategies to control for teosinte infestations including adoption of specific cultural practices such as manual control constructing false seedbeds, as well as adopting corn rotations with other annual and perennial crops. In spite of the potential negative impacts of this weed, little is known about what the optimal control strategies are, both from the private (e.g. the farm) and social (e.g. regulatory agencies) perspectives. In response, we develop a dynamic optimization model to identify the sequence of control strategies that minimize private and social costs under low- and high-infestation level scenarios, for a fifteen-year planning horizon. We calibrate the model using biological data from experimental trials and economic parameters collected from farmers in the region. Our results suggest the economic losses of teosinte infestation can reach up to 7444 and 8421 €/ha for low- and high-infestation scenarios if nothing is done to control it. In addition, results show that optimal private and social strategies are different. For example, under high-infestation levels, private losses are minimized at 26.5% by not controlling in years 1-2, use false seedbeds in year 3, planting alfalfa in years 4-8, and planting corn thereafter in the total area. In contrast, social cost are minimized at 27.9% by adopting rotations starting year, return to corn mono-cropping in half the area after year four. Results show false seedbed and manual controls, currently recommended by the regulatory agency in lowinfestation cases, are not socially optimal.

[ES] En los últimos años han proliferado en España operadores comerciales de drones que ofrecen sus servicios en agricultura. El presente estudio muestra diferentes problemas a los que los clientes pueden enfrentarse al utilizar los servicios de estas empresas comerciales emergentes. La experiencia en la contratación de diferentes vuelos en el marco de un proyecto I+D+i ha revelado que las empresas conocen perfectamente las plataformas y todo lo relacionado con el vuelo. Sin embargo, son pocas las que están familiarizadas con la agricultura y la teledetección o su conocimiento es limitado. Aunque algunas de las empresas que ofertan servicios de teledetección en agricultura carecen de los sensores apropiados, prácticamente todas disponen de cámaras RGB y bastantes de multiespectrales, siendo escasas las que ofertan térmico. Predominan las empresas que utilizan sistemas y programas cerrados para la planificación del vuelo, captura y procesado de las imágenes, siendo muy complicado disponer de especificaciones sobre los modelos y metodologías concretas que siguen para su obtención o solicitar productos a la carta diferentes a los estándares. Actualmente en España los vuelos comerciales con drones son operativos para adquirir imágenes que permiten la interpretación visual, identificación del estado de los cultivos, zonificación, generación de mapas de vigor, etc. con los que dar soporte y recomendaciones agronómicas basadas en cuantificaciones relativas. Sin embargo, todavía son pocas las empresas que pueden ofrecer productos más especializados con calidad suficiente para su integración en modelos cuantitativos de estimación de variables agronómicas. [EN] In the last years, the number of commercial drone operators that offer services to the agriculture sector have increased significantly in Spain. The present study shows the different problems that customers can face when using the services of these emergent commercial companies. The flight hiring experience, into a research, development and innovation project, has showed that companies have a good knowledge about platforms and everything related with programming flights. However, few companies are familiarized with agriculture and remote sensing or their knowledge is limited. Although some of these companies lack of appropriate sensors to offer remote sensing services in agriculture, all of them offer RGB cameras, most of them multispectral cameras, and only few companies offering thermal images. Most of the companies use closed systems and programs for flight planning, image capture and processing. Therefore it is difficult to the customer to obtain specifications about the models and specific methodologies used to produce the images and to request on-demand products different from the standard offered by the companies. Currently in Spain, commercial flights with drones are operative to acquire images for visual interpretation, crop status identification, zoning areas, obtain vigor maps, etc. to give support and agronomic recommendations based on relative measurability. However, there are still few companies offering more specialized products with sufficient quality for their integration in quantitative models of agronomical variables estimation. Este trabajo forma parte de actividades desarrolladas en el proyecto INNOVA-A1-077/15. 4 Pags.- 1 Fig. Peer reviewed

Employing a recursive dynamic computable general equilibrium (CGE) model of the Spanish economy, this study explicitly aims to characterise the potential impact of Kyoto and European Union environmental policy targets on specific agricultural activities up to 2020. The model code is modified to characterise the emissions trading scheme (ETS), emissions quotas and carbon taxes, whilst emissions reductions are applied to all six registered greenhouse gases (GHGs). Compared to a ‘business-as-usual’ baseline scenario, by 2020, GDP and employment fall 2.1% and 2.4%, respectively, whilst the retail price index rises 3.4%. In agriculture, the indices of output (4.3% fall), and supply price (7.7% rise) perform relatively worse, whilst there is a concomitant cumulative fall in aggregate farm incomes of €1,510 million by 2020. The more notable impact in agriculture is attributed to its relatively higher emissions intensity. Consequently, we record an agricultural marginal abatement cost estimate of €86 ton–1 of CO2 equivalent by 2020, which is consistent with other estimates in the literature. In addition, we find that the optimal mix of emissions reductions across specific agricultural sectors is a function of the degree of substitutability of their emitting activities. In light of estimated income losses within the strategically important farm sector, a final simulation contemplates an ‘agricultural cost-neutral’ emissions reduction policy akin to a cross compliance payment between 2013 and 2020. This is found to reduce food price rises, whilst altering the optimum mix of agricultural emissions reductions across specific agricultural activities. Empleando un modelo dinámico recursivo de equilibrio general computable (EGC) de la economía española, este estudio analiza el impacto de las políticas medioambientales de Kioto y de la Unión Europea (el acuerdo ‘20/20/20’), sobre distintas actividades agrarias hasta 2020. En comparación con el escenario de referencia, se pronostican caídas en el PIB y el empleo de un 2,1% y 2,4%, respectivamente, en 2020, mientras que el índice de precios al consumo sube un 3,4%. En agricultura, el índice de producción (que cae un 4,3%) y el de precios (aumenta un 7,7%), empeoran y además los ingresos acumulados de los agricultores bajan 1.510 millones de euros en 2020. El impacto más acusado en el sector agrario se atribuye a la mayor intensidad de sus emisiones donde se estima un coste marginal de reducción de 86 € t–1 de CO2 equivalente para 2020, lo cual es consistente con las estimaciones existentes en la bibliografía. Además se observa que la combinación óptima de reducción de emisiones en los diferentes sectores agrarios depende del grado de sustitución de las actividades emisoras. A la vista de las pérdidas de ingresos observadas en el sector agrario, se contempla un escenario de mitigación de coste-cero para los agricultores, semejante a un pago de condicionalidad, entre 2013 y 2020. Los resultados señalan una mitigación en el incremento de los precios de los alimentos y una redistribución en la combinación óptima de las emisiones en los sectores agrarios.