隨著氣候變化給農(nóng)業(yè)生產(chǎn)帶來了更多不確定性,，各國(guó)政府和商業(yè)團(tuán)體也越來越關(guān)注對(duì)作物產(chǎn)量的預(yù)測(cè)。據(jù)物理學(xué)家組織網(wǎng)7月22日?qǐng)?bào)道,，一個(gè)由日本,、美國(guó)、英國(guó)等多國(guó)人員組成的國(guó)際小組提出,，氣候數(shù)據(jù)可以在收獲前的幾個(gè)月幫助預(yù)測(cè)某些農(nóng)作物的歉收情況。相關(guān)論文發(fā)表在7月21日的《自然·氣候變化》上,。
該研究由日本國(guó)家農(nóng)業(yè)—環(huán)境科學(xué)研究所領(lǐng)導(dǎo),。研究人員發(fā)現(xiàn)，在大約1/3的全球耕地中,，溫度,、土壤水分與小麥、水稻的收獲產(chǎn)量之間存在密切關(guān)系,。用計(jì)算機(jī)模型可以提前3個(gè)月預(yù)測(cè)全球約20%的小麥和水稻產(chǎn)地是否會(huì)歉收,。
他們利用一個(gè)新的作物模型，結(jié)合溫度,、降水預(yù)測(cè)以及1983年到2006年的衛(wèi)星觀測(cè)數(shù)據(jù),，分析了玉米、大豆,、小麥和水稻4種作物,。模型顯示，對(duì)小麥和水稻的預(yù)測(cè)大部分是成功的,。對(duì)一些小麥和水稻主要出口國(guó)所在地區(qū),，如澳大利亞和烏拉圭等地，能提前幾個(gè)月預(yù)測(cè)作物歉收情況,。除了能預(yù)測(cè)由嚴(yán)重干旱及其他極端天氣導(dǎo)致的大規(guī)模作物減產(chǎn),，模型還能預(yù)測(cè)產(chǎn)量上的一些小變化。
從全球范圍來看,，雖然預(yù)測(cè)精度只有三成左右,，但研究人員希望通過完善預(yù)測(cè)參數(shù)來提高預(yù)測(cè)精度，并指出僅利用氣溫和土壤水分?jǐn)?shù)據(jù)就能夠預(yù)測(cè)到這個(gè)水平,，今后若能利用更加精密的氣象數(shù)據(jù),，可預(yù)測(cè)的區(qū)域?qū)⒏訌V泛。
論文合著者,、英國(guó)利茲大學(xué)教授安迪·查林諾說：“極端氣候的影響比小的氣候變動(dòng)更容易預(yù)測(cè),，在全球的許多地方，即使是產(chǎn)量上5%的變化,，該模型也能正確地模擬出來,。”
美國(guó)國(guó)家航空航天局戈達(dá)德生物圈科學(xué)實(shí)驗(yàn)室的莫莉·布朗解釋說,，氣候在作物產(chǎn)量與歉收中的作用或許更直觀，但要演示出來卻很困難,，因?yàn)樯鐣?huì)與經(jīng)濟(jì)因素起了主要作用,，包括農(nóng)業(yè)技術(shù)、化肥,、種子和灌溉設(shè)施,，是決定一個(gè)農(nóng)場(chǎng)產(chǎn)量的關(guān)鍵。但氣候因素仍會(huì)引起好壞年景的差異,。“我們?cè)噲D確定天氣因素在其中起了多大作用,。對(duì)于特定地區(qū)的特定作物來說，它造成了巨大差異,，尤其是對(duì)小麥,。這篇論文為我們提供了一種工具，有助于理解經(jīng)濟(jì)領(lǐng)域之外的變異性來源,。”
該研究項(xiàng)目旨在讓貧困國(guó)家的農(nóng)民能在好年景取得更好的收成,，在不好年景建立緩沖機(jī)制。布朗說：“我們可以制定一個(gè)新框架,，對(duì)衛(wèi)星數(shù)據(jù)和氣候預(yù)測(cè)模型做進(jìn)一步探索,。”比如，若能在水稻結(jié)籽甚至種植前,，預(yù)測(cè)未來是好年景,，農(nóng)民就能獲得貸款用于投資技術(shù)，以充分利用好天氣優(yōu)勢(shì),，保險(xiǎn)公司也會(huì)降低保費(fèi),；如果預(yù)測(cè)是壞年景，可能貸款變少而保費(fèi)提高,，并使決策者預(yù)先制定促進(jìn)糧食生產(chǎn)的舉措,，投資必要的基礎(chǔ)設(shè)施，它可能起到一個(gè)社會(huì)安全網(wǎng)的作用,。（生物谷 Bioon.com）
生物谷推薦的英文摘要
Nature Climate Change doi:10.1038/nclimate1945
Prediction of seasonal climate-induced variations in global food production
Toshichika Iizumi, Hirofumi Sakuma, Masayuki Yokozawa, Jing-Jia Luo, Andrew J. Challinor, Molly E. Brown, Gen Sakurai & Toshio Yamagata
Consumers, including the poor in many countries, are increasingly dependent on food imports1 and are thus exposed to variations in yields, production and export prices in the major food-producing regions of the world. National governments and commercial entities are therefore paying increased attention to the cropping forecasts of important food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years2, 3, 4. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We found that moderate-to-marked yield loss over a substantial percentage (26–33%) of the harvested area of these crops is reliably predictable if climatic forecasts are near perfect. However, only rice and wheat production are reliably predictable at three months before the harvest using within-season hindcasts. The reliabilities of estimates varied substantially by crop—rice and wheat yields were the most predictable, followed by soybean and maize. The reasons for variation in the reliability of the estimates included the differences in crop sensitivity to the climate and the technology used by the crop-producing regions. Our findings reveal that the use of seasonal climatic forecasts to predict crop failures will be useful for monitoring global food production and will encourage the adaptation of food systems toclimatic extremes.
