中国主要农作物种植农药施用温室气体排放估算

Estimate of greenhouse gases emission from pesticides usage in China's major crops

过去30年来我国农作物的播种面积并未产生太大变化,但病虫害的发生和防治次数却不断增加。根据6种中国主要农作物的病虫害发生情况,收集了相应的农药用量及其制造的温室气体排放量数据,估算了中国主要农作物在种植过程中,因对病虫害使用杀虫剂和杀菌剂而产生的温室气体排放量现状。结果表明,我国主要农作物小麦、水稻、玉米、马铃薯、油菜和棉花的每公顷病虫害防治时使用农药所产生的温室气体排放量分别是9.19(1.86—23.24)、20.54(2.03—50.95)、10.38(3.45—19.32)、5.91(2.15—18.34)、10.84(8.10—13.62)、19.51(5.11—49.01)kg CE hm^(-2)a^(-1),即水稻和棉花最高;但论单产农药温室气体排放量,则油菜和棉花远高于其余4种粮食作物。每年小麦、水稻、玉米、马铃薯、油菜和棉花的病虫害防治使用农药所产生的总温室气体排放量分别是220.8(44.7—558.4)、606.7(60.0—1505.1)、336.4(112.0—606.3)、30.9(11.2—96.0)、79.5(59.4—99.8)、96.4(25.2—242.2)Gg CE,总计1.37(0.31—3.13)Tg CE。将以上6种作物的病虫害防治情况外推到全国农作物,则我国一年因为农作物病虫害防治而产生的温室气体排放量为2.13(0.48—4.85)Tg CE。另外由于缺乏草害面次数据而没有包括除草剂本分,所以以上数字仍是低估。病虫害防治由于作物本身、防治对象、防治方法以及药剂用量的固有差异,导致农作物病虫害防治的温室气体排放量计算结果存在着较大的不确定性,目前基于自下而上农户调查的估算方法无法克服这些问题,更精确的估算需要自上而下的企业级调查数据。

Over the past 30 years, China＇s total area sown with crops did not change significantly. Meanwhile, the occurrences of crop pests and diseases as well as the control （treatment） areas have increased drastically. China has become the world＇s largest pesticide manufacturer and consumer since 2005 and the up-trend is expected to continue. Correspondingly, the greenhouse gases （GHGs） emission resulting from pesticide usage may also increase fast and become an important part of indirect GHGs emission in agriculture. However, domestic GHGs emission parameters from pesticide usage were rarely reported in analysis of China＇s agricultural life-cycle, which leads to considerable uncertainty in studies related to agricultural indirect GHGs emission. In this study, GHGs emission from pesticides manufacture was estimated by summing up the global warming potential of GHGs emitted from four processes including manufacture of active ingredients, formulation of emulsifiable oils/wettable powders/granules, packaging, and transport. According to the occurrence of pests and diseases in six major crops （wheat, rice, maize, potato, oilseed rape, and cotton） in China, the amount of current GHGs emissions induced by usage of insecticides and fungicides specific for these crops was assessed based on the integration of available information on the use of relevant pesticides and GHGs emissions from their manufacturing. Our estimation indicated that the GHGs emission pertaining to pests and diseases control was the highest for rice and cotton （ 20.54 [ 2.03--50.95 ] and 19.51 [ 5.11--49.01 ~, respectively） followed by oilseed rape （ 10.84 [ 8.10--13.62 ~ ）, maize （10.38 [3.45--19.32]）, wheat （9.19 [1.86--23.24] ）, and potato （5.91 [2.15--18.341 kg carbon equivalent ICE] per hectare each year）. Different crop pests and diseases contributed differently to crop＇s GHGs emission from pesticides usage： for maize and cotton, pests were the main contributors （especially maize borer, maize earworm, and cotton plant- bug） ; for oilseed rape and potato, diseases were the main contributors （especially potato late blight and rape sclerotinia rot） ; and for wheat and rice, pests and diseases both contributed equally to the total emission （ especially wheat aphid, wheat midge, wheat red spider, wheat powdery mildew, wheat borer, rice blast, and rice sheath blight）. Meanwhile, as to the contributed far less than cotton and oilseed rape. Correspondingl scab, rice plant hopper, rice leaf roller, rice striped stem pesticide GHGs emission per unit yield, all four grain crops y, the overall emission due to the insect pests and diseases control measures for each of the analyzed crops in China was ： 220.8 （ 44.7-558.4 ）, 606.7 （ 60.0- 1505.1 ）, 336.4 （112.0-606.3）, 30.9 （11.2-96.0）, 79.5 （59.4-99.8）, and 96.4 （25.2-242.2） Gg CE/a for wheat, flee, maize, potato, oilseed rape, and cotton, respectively, with a total amount of 1.37 （0.31--3.13） Tg C E/a. It should be noted that these results are underestimation of China＇s actual pesticide GHGs emission since the herbicides were not considered because of the unavailability of weed treatment data. Owing to the variation in the characteristics of crop pests and diseases control measures （ including various conditions of crops, pests, and diseases, wide range of pesticide and fungicide choices and their legal dosage）, non-negligible uncertainties still exist in our current bottom-up estimates based on farmer surveys. More accurate estimation requires implementation of top-down methods and data based on enterprise-level surveys.