Crop protection

The Complete Grape Growing System

The Complete Grape Growing System

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Pesticides use multiplied by a factor of 32 between 1950 and 1986, with developing countries now accounting for a quarter of the world's pesticide use. Inappropriate and excessive use can cause contamination of both food and environment and, in some cases, damage the health of farmers and consumers. Pesticides also kill the natural predators of pests, allowing them to multiply; meanwhile the number of

Fig. 26.3. Numberof insects (bold), pathogens (dotted) and weeds (thin) resistant to pesticides (www.fao.org)

Fig. 26.3. Numberof insects (bold), pathogens (dotted) and weeds (thin) resistant to pesticides (www.fao.org)

Fig. 26.4. Geographical repartition of simulation models developed since '80
Table 26.4. Crops considered for the application of epidemiological models (Friesland and Orlandini 2006).

Category

Crop

Cereals

wheat, barley, rye, oats, maize, sorghum, rice

Row crops and other

potatoes, sugar beet, oilseed rape, soybean, sunflower, hop, tobacco, alfalfa

Vegetables

cabbage, onions, leek, tomato, carrot, celery, bean, paprika, lettuce, pea, turnip, aubergine

Fruit

apple, grapevine, citrus, plum, pear, cherry, melon, olive, strawberry, watermelon

Other

elm, mustrad, pinewood, rose, chestnut, almond, poplar, oak

pest species with resistance to pesticides has increased from a handful 50 years ago to over 700 now (Fig. 26.3).

A large number of simulation models has been formulated in the last decades, starting during the '80. tte higher contribution is from Europe and North America, while Asia (mainly oriented to rice application), Africa, Oceania and South America show a smaller activity (Fig. 26.4).

tte main crops have been studied, from field annual crops, to forestry, trees and flowers (Table 26.4).

P.Rada is a project funded by the European Community in the frame of the initiative "Interreg IIIA Italia-Slovenia 2000-2006" created to promote an operational collaboration between the two neighbouring countries.

In particular, the interested area includes Friuli Venezia Giulia region (North East part of Italy) and the eastern part of Slovenia, ttis system is applied on the entire regional area and represents a fundamental DSS for most agricultural workers both in public administrations and in farms. It is the result of the integrated use of epidemiological simulation model, remote sensing and the GIS. It is composed by three subroutines: a module for meteorological data spatialisation, a leaf wetness and a disease development simulation model.

tte necessary agrometeorological data are collected by ground stations scattered on the two countries, spatialised and integrated with rainfall data collected by the meteorological radar of Fossalon di Grado (Italy), tte territorial information obtained is then used by the system to feed two agrometeorological models: the first for the estimation of leaf wetness and the second for the simulation of grapevine downy mildew, tte main output is represented by daily maps containing operational indications about the current meteorological situation, the presence and the stage of downy mildew development and the evaluation of the potential risk (Fig. 26.5). In the current system great attention is paid to grapevine downy mildew, nevertheless its modular structure allows to consider other biological processes thanks to new algorithms and subroutines.

Fig. 26.5 Examples of output maps of rainfall (a), leaf wetness duration (b) and number of current downy mildew infections (c) in Friuli Venezia Giulia region (Italy)

As far as remote sensing is concerned, it is important to emphasize the fact that it is a very helpful tool and research efforts should be supported, particularly with regard to operational applications. Indeed, the use of these techniques allows to obtain accurate data estimations, reducing both expenses and installation/maintenance works.

Nevertheless, it is acknowledged that both radar and, particularly, satellite data frequently provide discordant rain estimates as compared to traditional rain gauges. As a matter of fact, remote sensing provides indirect phenomenon estimations and the measures are affected by a low spatial and temporal accuracy. On the other hand, rain is an extremely variable unit both in spatial and temporal terms, ttese differences in measurement principles greatly amplify the disagreement due to instrument characteristic errors. As a result, satellite or radar estimates cannot exactly reply rain gauge measurements. Nevertheless, an integration between these methodologies is desirable, given that remote sensing data are characterized by high temporal resolution and spatial continuity.

www.fao.org)"/>
Fig. 26.6. Number and percentage of chronically undernourished (grey) on total population (black) in developing regions 19901992 (www.fao.org)

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