近年來(lái)，早疫病的流行頻繁導(dǎo)致馬鈴薯作物嚴(yán)重減產(chǎn)。當(dāng)天氣條件有利時(shí)，這種真菌病會(huì)迅速發(fā)展，迫使農(nóng)民使用殺菌劑。利米亞是西班牙最大的馬鈴薯生產(chǎn)區(qū)之一。通常，早期疫病流行是使用預(yù)先制定的日程表來(lái)控制的。這種策略成本高昂，并且會(huì)影響農(nóng)業(yè)區(qū)的環(huán)境。目前還沒(méi)有農(nóng)民使用決策支持系統(tǒng)來(lái)管理早疫病。因此，本研究的目的是根據(jù)植物或/和病原體要求和天氣條件評(píng)估不同的早疫病預(yù)測(cè)模型，以檢查它們對(duì)預(yù)測(cè)早疫病最初癥狀的適用性，這是確定第一種殺菌劑的時(shí)間所必需的應(yīng)用。為此，在五個(gè)作物季節(jié)監(jiān)測(cè)天氣、物候和疾病癥狀。在植物出苗后 37 至 40 天，在開花期開始出現(xiàn)第一個(gè)早疫病癥狀�；�于植物的預(yù)測(cè)模型提供了最好的結(jié)果。具體而言，具有 1.4 個(gè)風(fēng)險(xiǎn)單位和成長(zhǎng)度天數(shù)（361 個(gè)累積單位）的 Wang-Engel 模型提供了最佳預(yù)測(cè)�；�于病原體的模型顯示出保守的預(yù)測(cè)，而結(jié)合植物和病原體特征的模型預(yù)測(cè)第一次早疫病襲擊明顯較晚。
 

五個(gè)作物季節(jié)的天氣參數(shù)和物候變化

Suitability of Early Blight Forecasting Systems for Detecting First Symptoms in Potato Crops of NW Spain

Abstract: In recent years, early blight epidemics have been frequently causing important yield loses

in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing

the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain.

Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later.
 

Pessl植物生理生態(tài)監(jiān)測(cè)系統(tǒng)的全套監(jiān)測(cè)系統(tǒng)和在線平臺(tái)FieldClimate適用于所有氣候區(qū)，可用于各種行業(yè)和各種用途——從農(nóng)業(yè)到研究、水文、氣象、洪水警報(bào)等。iMetos植物生理生態(tài)監(jiān)測(cè)系統(tǒng)已經(jīng)成為一個(gè)全球品牌，使用持續(xù)時(shí)間更長(zhǎng)，性能更好，是通用的天氣監(jiān)測(cè)設(shè)備，具有早期識(shí)別和警報(bào)功能（有SMS手機(jī)提醒功能）；可以用來(lái)計(jì)劃、控制和管理復(fù)雜的獨(dú)立氣象過(guò)程。該監(jiān)測(cè)系統(tǒng)專為不同氣候區(qū)域的多種任務(wù)而設(shè)計(jì)。其可以安裝多達(dá)600個(gè)傳感器，如土壤和空氣濕度、溫度、降雨、風(fēng)速、風(fēng)向、葉片 濕度、總體輻射等傳感器。
 

Pessl植物生理生態(tài)監(jiān)測(cè)系統(tǒng)的數(shù)據(jù)采集工作站可以將這些數(shù)據(jù)無(wú)線傳輸?shù)桨踩�的互�?lián)網(wǎng)數(shù)據(jù)庫(kù)上。該數(shù)據(jù)庫(kù)是優(yōu)秀的數(shù)據(jù)存儲(chǔ)和處理平臺(tái)。用戶獲得登錄密碼后，可以從世界任何地方的互聯(lián)網(wǎng)終端登錄并獲得這些數(shù)據(jù)、報(bào)告和圖表。測(cè)量的信息來(lái)源于傳感器所在的位置。使用者可以從網(wǎng)站上一個(gè)區(qū)域可輸入或修改閾值和電話號(hào)碼。操作無(wú)需專門軟件。
 

Pessl植物生理生態(tài)監(jiān)測(cè)系統(tǒng)僅需要有效的GPRS協(xié)議用于數(shù)據(jù)傳輸，在站點(diǎn)所在處也需要網(wǎng)絡(luò)的充分覆蓋。該系統(tǒng)是一組多功能、模塊化配置的系統(tǒng)，運(yùn)行完全免維護(hù)。該工作站采用太陽(yáng)能充電電池。工作站可以連接多種傳感器。即插即用模式便于工作站擴(kuò)展傳感器數(shù)目。
 

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