【摘要】：精準(zhǔn)農(nóng)業(yè)強調(diào)通過對農(nóng)田資源的分布式調(diào)控,盡可能均衡、合理地利用其生產(chǎn)潛力,獲取盡可能高的經(jīng)濟(jì)產(chǎn)量或顯著的社會效益。掌握農(nóng)田土壤水分和養(yǎng)分的空間分布規(guī)律是分布式調(diào)控的前提條件,而基于有限的采樣數(shù)據(jù)進(jìn)行空間插值則是掌握農(nóng)田土壤水分和養(yǎng)分變異規(guī)律的有效途徑,對于實現(xiàn)農(nóng)田的精確灌溉和變量施肥具有重要意義。本文以江蘇省揚州市北部某農(nóng)田上161個采樣點的含水量、全氮量、有機質(zhì)、堿解氮、速效鉀、速效磷含量為例,針對研究區(qū)土壤變量的空間變異特征,結(jié)合所采用的空間插值方法的特點,開展了如下研究：①利用研究區(qū)不同田塊之間土壤特性差異顯著、同一田塊無顯著差異的特點,用同一田塊內(nèi)土壤變量的分布特征表達(dá)待估點估值的不確定性(即構(gòu)造軟數(shù)據(jù)),并將此軟數(shù)據(jù)和實測硬數(shù)據(jù)結(jié)合,利用現(xiàn)代地質(zhì)統(tǒng)計學(xué)方法——貝葉斯最大熵法(BME)模擬土壤變量的空間分布(該方法以下簡稱MVBME法)；②利用神經(jīng)網(wǎng)絡(luò)方法良好的非線性表達(dá)能力,借助集成神經(jīng)網(wǎng)絡(luò)系統(tǒng)估計待估點的不確定性(即構(gòu)造軟數(shù)據(jù)),并將其結(jié)果融入BME法中,用融入該軟數(shù)據(jù)的BME法(以下簡稱NNEBME法)模擬土壤變量的空間分布。③采取多種隨機抽樣方案(建模樣本和驗證樣本),將以上空間模擬結(jié)果分別與徑向基函數(shù)神經(jīng)網(wǎng)絡(luò)法(RBF)、集成神經(jīng)網(wǎng)絡(luò)法(NNE)、普通克立格法(OK)、殘差克立格法(RK)的估值結(jié)果進(jìn)行比較。得出的主要研究結(jié)果如下：1)研究區(qū)內(nèi)土壤含水量呈弱變異性,養(yǎng)分均屬中等程度變異。且研究區(qū)內(nèi)整個試驗田的變異性比單塊要大很多,不同田塊之間土壤特性差異達(dá)到極顯著,同一田塊多屬無顯著性差異。2)特異值處理前,含水量、全氮量、有機質(zhì)、堿解氮都近似服從正態(tài)分布,速效鉀與速效磷不服從正態(tài)分布,處理特異值后土壤水分及養(yǎng)分的空間分布均近似服從正態(tài)分布。3)MVBME法對研究區(qū)內(nèi)土壤水分與養(yǎng)分進(jìn)行空間插值,并將結(jié)果與RBF法、RK法和OK法的插值結(jié)果進(jìn)行比較得出：MVBME法的平均誤差(ME)為四種插值方法中最小,估計方差(MSE)相較RBF法能降低23.77%-69.14%：相比較RE法,能降低0.41%-56.17%：與OK法相比,MVBME法能使有機質(zhì)、堿解氮、速效鉀、速效磷的MSE降低6.24%～52.37%,水分與全氮量在大部分情況下能降低10.25%-38.18%。四種插值方法里.MVBME法最接近于無偏估計,且對變量空間波動程度反映最精確。4)NNEBME法對研究區(qū)土壤含水量與養(yǎng)分進(jìn)行空間差值,其結(jié)果與NNE法、RK法和OK法進(jìn)行比較得出：對于不同的土壤變量,NNEBME法估值的ME最接近于零,近似無偏估計；與NNE法比較,MSE縮小1.64%～45.20%,與OK法、RK法比較,除土壤水分外,NNEBME法使MSE縮小0-40.05%：并且隨著已知點(即建模數(shù)據(jù)樣本容量)個數(shù)的減少,NNEBME法的插值優(yōu)勢更為突出；MSE的組成分析表明,NNEBME法對變量均值的估計與波動程度的描述更為精確。本文利用農(nóng)田土壤變量的空間變異特征和估值方法的特點,構(gòu)建了能為BME法有效利用的軟數(shù)據(jù),不僅拓展了現(xiàn)代地質(zhì)統(tǒng)計學(xué)的BME方法在農(nóng)業(yè)水土科學(xué)領(lǐng)域的應(yīng)用范圍,而且為農(nóng)田土壤水分和養(yǎng)分空間分布模擬精度的改善提供了新思路。
[Abstract]:The precision agriculture emphasizes the distribution and regulation of the farmland resources, as well as possible balance and reasonable utilization of the production potential of the farmland resources, so as to obtain the highest possible economic output or remarkable social benefit. The spatial distribution of soil moisture and nutrients in farmland is a prerequisite for distributed regulation, and spatial interpolation based on limited sampling data is an effective way to master the law of soil moisture and nutrient variation of the farmland. It is of great significance to realize the precise irrigation and variable fertilization of the farmland. In this paper, the water content, total nitrogen content, organic matter, alkaline solution nitrogen, quick-acting potassium and quick-acting phosphorus content of 161 sampling points on a farmland in the northern part of Yangzhou city of Jiangsu Province are taken as an example. in that invention, the soil characteristic difference between the different field blocks in the study area is obviously different, the characteristic of no significant difference of the same field block is used, the uncertainty of the estimation of the point to be evaluated (i. e., the soft data is construct) is expressed by the distribution characteristic of the soil variable in the same field block, and the soft data and the measured hard data are combined, The spatial distribution of the soil variable is simulated by the modern geostatistical method _ Bayesian Maximum Entropy Method (BME) (this method is referred to as the MVBME method), and the nonlinear expression ability of the soil variable is good by using the neural network method. The uncertainty of the point to be evaluated (i.e., the soft data) is estimated by the integrated neural network system, and the result is integrated into the BME method, and the spatial distribution of the soil variable is simulated by the BME method (hereinafter referred to as the NNEBME method) incorporated into the soft data. The results of the above-mentioned space simulation are compared with the estimation results of the radial basis function neural network method (RBF), the integrated neural network method (NNE), the ordinary Kriging (OK) and the residual Kriging (RK). The main results of the study are as follows:1) The soil water content in the study area is weak and the nutrient is of medium degree variation. and the variation ratio of the whole test field in the study area is much larger than that of the single block, the difference of the soil characteristics between the different field blocks is very significant, and there is no significant difference in the same field block.2) Before the specific value is treated, the water content, the total nitrogen amount, the organic matter and the alkaline solution nitrogen are all approximately subject to normal distribution, The spatial distribution of the soil water and nutrients in the study area was estimated by MVBME method, and the results were compared with the results of the RBF method, the RK method and the OK method. The mean error (ME) of the MVBME method is the least in the four interpolation methods, and the estimated variance (MSE) can be reduced by 23.77%-69.14% compared with the RBF method. Compared with the OK method, the MVBME method can reduce the MSE of the organic matter, the alkaline solution nitrogen, the quick-acting potassium and the quick-acting phosphorus by 6.24% to 52.37%, Water and total nitrogen can be reduced by 10.25% to 38.18% in most cases. In four interpolation methods. The MVBME method is closest to the unbiased estimation, and the spatial difference of soil water content and nutrients in the study area is reflected by the NNEBME method. The results are compared with the NNE method, the RK method and the OK method. Compared with the NNE method, the MSE is reduced by 1.64% to 45.20%, compared with the OK method and the RK method, the MSE is reduced by 0-40.05% in addition to the soil moisture, and the interpolation advantage of the NNEBME method is more prominent with the reduction of the number of known points (i.e., the sample capacity of the modeling data); the analysis of the composition of the MSE shows that, The NNNME method is more accurate to estimate the mean value of the variable and the degree of fluctuation. By using the characteristics of the spatial variation and the estimation method of the farmland soil variable, the soft data which can be effectively utilized by the BME method is constructed, and the application range of the BME method of the modern geostatistics in the field of agricultural water and soil science is not only expanded, But also provides a new thought for improving the soil moisture and the distribution of the nutrient spatial distribution of the farmland.
【學(xué)位授予單位】：揚州大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：S152.7;S158

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本文編號：2505459