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农田养分空间变异特征及精准/分区管理技术研究

Study on Spatial Variability and Site-specific Management of Soil Nutrients Within Fields

【作者】 黄立梅

【导师】 黄绍文;

【作者基本信息】 中国农业科学院 , 植物营养学, 2010, 硕士

【摘要】 土壤养分供应与作物养分需求在空间上的不协调是限制作物高产和养分高效利用的关键因素之一,但目前结合不同网格取样尺度、土壤养分、作物产量和养分吸收,探讨农田养分空间变异特征及精准/分区平衡施肥技术的报道甚少。本文应用GPS、GIS等有关信息技术和地统计学方法,研究了规模经营的黑龙江江川农场水稻、分散经营的河北衡水冬小麦-夏玉米和分散经营的吉林陶家春玉米三个试区三种网格(50 m×50 m、100 m×100 m和150 m×150 m)取样尺度下农田土壤养分空间变异特征及其与作物产量的空间关系和精准/分区平衡施肥技术的应用效应,为发展适合我国国情的农田土壤养分精准/分区管理技术提供理论基础,主要研究结果如下:1.农田土壤养分空间变异特征采用传统统计和地统计相结合的方法,对江川农场水稻试区、衡水冬小麦-夏玉米试区和陶家春玉米试区农田土壤养分空间变异特征进行了研究。规模经营的江川农场水稻试区土壤养分主要限制因子是N、P、K和Zn;三种网格(50 m×50 m、100 m×100 m和150 m×150 m)取样尺度下试区9个水稻田块间土壤主要养分含量差异总体上均显著,但同一田块三种网格取样尺度下土壤主要养分含量差异总体上均不显著;三种网格取样尺度的同一土壤速效养分在空间分布上具有较明显的空间相似性。表明按150 m×150 m网格进行土壤取样,能对规模经营稻田不同田块土壤主要养分状况进行正确评价;对规模经营的江川农场试区稻田可按田块(6.3-12.9 hm2/田块)为管理单元进行土壤养分分区管理。分散经营的衡水冬小麦-夏玉米试区土壤养分管理的重点是N、P和K,对土壤Zn也要引起一定重视。在50 m×50 m、100 m×100 m和150 m×150 m三种网格取样尺度下,试区同一土壤养分含量概况基本无差异,同一土壤养分在空间分布上具有较明显的空间相似性。显示按150 m×150 m网格进行土壤取样,能对分散经营冬小麦-夏玉米试区农田土壤主要养分状况进行比较正确的评价,大大降低成本和工作量。分散经营的陶家春玉米试区土壤养分主要限制因子为N、P、K和Zn;试区同一生产小组三种网格(50 m×50 m、100 m×100 m和150 m×150 m)取样尺度间土壤主要养分含量差异总体上不显著;生产小组间50 m×50 m和100 m×100 m两种取样尺度的土壤速效P和Zn含量差异显著;三种网格取样尺度的同一土壤速效养分在空间分布上具有较明显的空间相似性。表明按150 m×150 m网格进行土壤取样,能对试区不同生产小组土壤OM和速效K状况进行正确评价,而对土壤速效P状况的评价被低估,对土壤速效Zn状况的评价被高估;对分散经营的陶家试区土壤OM和速效K可按生产小组(18.1-34.8 hm2/生产小组)为管理单元、土壤速效P和Zn可将生产小组分成2个区域进行土壤养分分区管理。在“十二”期间及以后,在东北平原、华北平原等粮食主产区发展适度规模经营前景广阔。上述结果显示,对分散经营的吉林陶家和河北衡水试区进行适度规模经营养分管理基本可行(对土壤主要养分可将生产小组分成1-2个区域,适度规模经营面积一般100亩以上),也易于指导平衡施肥和有利于提高生产效益。2.土壤养分空间变异性与作物产量的关系在上述三个试区按100 m×100 m网格测定作物籽粒和秸秆产量,并采集籽粒和秸秆样品,初步探讨了作物产量空间变异特征及其与养分吸收和土壤养分的空间关系。结果表明,不同经营体制和生产条件下作物籽粒产量和养分吸收总量的空间变异均较大,变异系数分别在9.7%- 15.8%和8.2%-21.1%之间。作物籽粒产量空间变异与作物整个生育期N、P2O5和K2O吸收总量空间变异之间密切相关,相关系数分别在0.76-0.87、0.68-0.90和0.35-0.65之间。土壤速效养分是作物吸收养分的最主要来源之一,水稻、冬小麦、夏玉米和春玉米整个生育期间氮和磷吸收总量空间变异与种植前土壤OM或相应土壤速效氮或磷的空间变异之间呈显著或一定的正相关,而冬小麦钾吸收量与种植前土壤速效钾含量之间呈极显著的正相关。土壤养分供应状况是作物产量潜力发挥最为重要的因素之一,作物籽粒产量的空间变异与土壤OM和速效养分的空间变异之间有显著的或一定的相关性。江川农场试区水稻籽粒产量与土壤OM和速效N含量之间呈显著或极显著的正相关;河北衡水试区冬小麦籽粒产量与土壤OM含量之间呈极显著的正相关,与土壤速效磷与锌含量之间均呈一定的正相关,而夏玉米籽粒产量与土壤NH4+-N含量之间呈极显著的正相关;吉林陶家试区春玉米籽粒产量与土壤OM、NO3--N和速效Zn含量之间呈显著或极显著的正相关。3.作物适宜氮磷用量及精准/分区平衡施肥技术针对衡水和陶家两试区氮磷不合理施用、肥料利用率低等问题,对两试区中等土壤肥力区域氮和磷的适宜用量进行了探讨。结果表明,在河北衡水试区中等土壤肥力区域,冬小麦适宜N和P2O5用量范围分别为220-260和90-110 kg/hm2,夏玉米适宜N和P2O5用量范围分别为220-280和95-115 kg/hm2;在吉林陶家试区中等肥力区域,春玉米适宜N和P2O5用量范围分别为170-190和60-75 kg/hm2。在明确上述试区土壤养分空间变异特征、养分合理施用技术等的基础上,应用土壤养分信息化管理和高产高效推荐平衡施肥咨询服务系统,提出了作物高产高效精准/分区平衡施肥技术。田间校验试验表明,精准/分区平衡施肥技术能显著提高作物产量、经济效益和氮肥利用率。与习惯施肥相比,江川农场试区水稻分区平衡施肥技术增产4.3%-11.2%,增收778.70-1532.00元/hm2,提高氮肥利用率12.6-14.0个百分点;河北衡水试区冬小麦分区平衡施肥技术增产6.7%,增收992.5元/hm2,提高氮肥利用率15.2个百分点;夏玉米分区平衡施肥技术增产5.3%-9.0%,增收454.19-464.92元/hm2,提高氮肥利用率8.4-10.7个百分点;吉林陶家春玉米分区平衡施肥技术增产7.3%-8.9%,增收1280.94-1647.78元/hm2。

【Abstract】 Spatial unsynchronization of nutrient supply and demand was one of the most important factors influencing high yield of crops and efficient use of nutrients. At present, little information is available on spatial variability and site-specific management of soil nutrients in main grain production areas from the viewpoint of grid-sampling scales, soil nutrients, crop yield and nutrient uptake being systematically considered. In this research, a rice production area (collective contract management system) of Jiangchuan farm in Heilongjiang province, a winter wheat - summer corn production area (family responsibility management system) of Hengshui in Hebei province, and a spring corn production area (family responsibility management system, rain-fed condition) of Taojia in Jilin province were selected as three experimental areas. Soil nutrient spatial variability characteristics under three grid-sampling scales (50 m×50 m, 100 m×100 m and 150 m×150 m ) in the three study areas, spatial relationship among soil nutrient contents, total nutrient uptake rates and crop yields, and crop response to site-specific balanced fertilization were studied using information technology, such as GIS and GPS, and geo-statistics. This will give scientific bases to develop site-specific nutrient management techniques within fields suitable to Chinese status of collective contract and family responsibility management systems. The main results obtained are summarized as follows:1. Spatial variability of soil nutrientsSpatial variability of soil nutrients in the three study areas were evaluated using traditional statistics and geo-statistics. In the rice production area of Jiangchuan farm under the collective contract management system, soil nutrient limiting factors were N, P, K and Zn. Significant differences in contents of soil nutrients among 9 fields were found in the study area under three grid-sampling scales. Whereas insignificant differences in contents of each nutrient in soils were observed under the three grid-sampling scales. Distinct spatial distribution similarity for each soil nutrient under the three grid-sampling scales was found with relatively high contents in some areas of the study area and relatively low contents in other areas. These showed that status of main soil nutrients under the collective contract management system can be evaluated on 150×150-m grid-sampling scale, and it was technically feasible to develop regionalized N, P, K and Zn nutrient management at the level of the field in the study area.In the winter wheat - summer corn production area of Hengshui under family responsibility management system, the emphasis on soil nutrient management was focused on N, P and K, and the management of Zn should also be given some attention. Insignificant differences in status (average content and C.V.) of each nutrient in soils were found in the study area under three grid-sampling scales. Distinct spatial distribution similarity for each soil nutrient under the three grid-sampling scales was observed with relatively high contents in some areas of the study area and relatively low contents in other areas. These indicated that status of main soil nutrients under family responsibility management system can be evaluated on 150×150-m grid-sampling scale, with the costs of soil sampling, soil testing, etc. decreasing significantly as sampling density decreasing.In the spring corn production area of Taojia under family responsibility management system, soil nutrient limiting factors were N, P, K and Zn. Insignificant differences of each nutrient in soils were generally found in the study area under three grid-sampling scales. Whereas significant differences in contents of soil P and Zn among three production groups were observed under 50×50-m and 100×100-m grid-sampling scales. Distinct spatial distribution similarity for each soil nutrient under the three grid-sampling scales were found, with relatively high contents in some areas of the study area and relatively low contents in other areas. These showed that the status of soil OM and K under family responsibility management system can be evaluated on 150×150-m grid-sampling scales, but the status of soil available P was undervalued and the status of soil available K was overvalued. It was technically feasible to develop regionalized OM and K management at the level of production group, and for soil available P and Zn, each production group could be regionalized into 2 management units, with each unit consisted of connected farmer plots.The moderate collective contract production management in main grain production regions of Northeastern Plain and North-central Plain has a broad prospect from 12th five-year plan. The results indicated that it was technically feasible to develop moderate collective production management of soil nutrients in Taojia of Jinlin study area and Hengshui of Hebei study area under family responsibility management system (Each production group could be regionalized into 1-2 management units for the main soil nutrients, with each unit consisted of connected farmer plots. The area of moderate collective production management is generally more than 6.7 ha ), which is helpful for extending balanced fertilization and enhancing economic income.2. Spatial relationship between soil nutrient contents and crop yieldsBased on the determined yields of crop grain and straw and the collected samples on a 100 m×100 m grid in the three study areas, spatial variability of crop grain yields and the spatial relationship among grain yields, total nutrient uptake rates and soil nutrient contents were analyzed. The results showed that significant spatial variability of crop grain yields and total nutrient uptake rates was found under different management systems (the collective contract management system and the family responsibility management system), and different conditions of agricultural production, with respective C.V. of crop grain yields and total nutrient uptake rates ranging from 9.7% to 15.8% and from 8.2% to 21.1%. Spatial variability of crop grain yields was closely correlated with total uptake rates of nitrogen, phosphorus (P2O5) and potassium (K2O) during growth period of crop, with respective correlation coefficients being 0.76-0.87, 0.68-0.90 and 0.35-0.65.Soil available nutrient was identified as significant sources of crop nutrient uptake. Spatial variability of total uptake rates of nitrogen and phosphorus during growth period of rice, winter wheat, summer corn and spring corn was closely and positively correlated with soil OM contents or corresponding available N or P contents in soils prior to crop seeding. Whereas spatial variability of total potassium uptake rates during growth period of winter wheat was significantly and positively correlated with soil available K contents.Soil nutrient status was one of the most important factors affecting crop yield potentials. Spatial variability of crop grain yields was positively correlated with contents of soil OM and available nutrients. Rice grain yields in Jiangchuan farm study area was closely and positively correlated with contents of OM and available N in soils. Grain yields of winter wheat in Hengshui study area was closely and positively correlated with soil OM contents, and a positive correlation relationship between grain yields and contents of soil available P and Zn was not significant at p < 0.05, respectively. Whereas grain yields of summer corn was closely and positively correlated with soil NH4+-N contents in Hengshui study area. Grain yields of spring corn in Taojia study area was closely and positively correlated with contents of OM, NO3--N and available Zn in soils.3. Proper nitrogen and phosphorus application rates and crop response to the site-specific balanced fertilizationField experiments were conducted to determined proper nitrogen and phosphorus application rates under medium soil fertility in Hengshui and Taojia study areas. The results showed that respective proper N and P2O5 application rates under medium soil fertility in Hengshui study area ranged from 220 to 260 and from 90 to 110 kg/ha for winter wheat, and from 220 to 280 and from 95 to 115 kg/ha for summer corn. Proper N and P2O5 application rates of spring corn under medium soil fertility in Taojia study area ranged from 170 to190 and from 60 to 75 kg/ha, respectively.Site-specific balanced fertilization techniques for high yield and high quality crop production in the three study areas were developed based on the regionalized soil nutrient GIS maps and a computerized fertilizer recommendation system. The results from field experiments showed that significant increase of grain yield, economic income and recovery rate of applied N were observed for the site-specific balanced fertilization. In Jiangchuan farm study area of Heilongjiang, site-specific balanced fertilization techniques increased rice yield, income and nitrogen recovery rate by 4.3%-11.2%, 778.70-1532.00 RMB Yuan/ha and 12.6-14.0 percentage point, respectively. In Hengshui study area of Hebei, yield, income and nitrogen recovery rate by site-specific balanced fertilization techniques respectively increased by 6.7%, 992.5 RMB Yuan/ha and 15.2 percentage point for winter wheat, and by 5.3%-9.0%, 454.19-464.92 RMB Yuan/ha and 8.4-10.7 percentage point for summer corn. In Taojia study area of Jilin, site-specific balanced fertilization techniques increased spring corn yield and income by 7.3%-8.9% and 1280.94-1647.78 RMB Yuan/ha, respectively.

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