English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Most crop models use climatic data recorded from weather stations located on standard surfaces such as grass. These environmental conditions represent meso-climates typical of the region but often do not reflect the crop micro-climate. The aim of this paper is to show the effect of the envi- ronment surrounding the weather stations on the variability of climatic data and to estimate the impact of these variations on different outputs of crop models (crop cycle length, yield, irrigation and quantities of fertilizer). The crop model STICS was used with different management strategies. Sim- ulations were performed using climatic data measured at 5 meteorological stations surrounded by different environments (wet grassland, dry area, wheat, soybean and irrigated wheat) in a small agri- cultural region in the south-east of France. The distances between the stations ranged from 3 km to a maximum of 10 km. The region is flat and has 1 weather station of the conventional national synop- tic network. The measurements show that, even at such a small regional scale, the spatial variability due to the environment surrounding a weather station is important and should be taken into account. Significant discrepancies were observed both for yield and irrigation, as well as crop cycle length, according to the climatic data considered. Differences in soil moisture produce the greatest differ- ences in air temperature between the weather stations and, consequently, the greatest discrepancies between the outputs of the crop models.

Impact of local climate variability on crop model estimates in the south-east of France