紫色土丘陵区农田土壤不同坡位取样单元确定研究
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摘要
采样调查是获取土壤信息及空间分布的基本方法,但土壤样品采集是一项费时、费力和耗财的活动,用尽可能少的样品获取准确的土壤信息就成为土壤调查追求的目标。我国西南紫色土丘陵区同源母质发育的旱坡地土壤,在作物类型、气候条件相同,田间管理措施基本一致的条件下,土壤理化性质仍具有典型的空间异质性,这表明地形对土壤属性具有显著的影响,在进行土壤采样时必须要将地形因素考虑在内。同时,由于紫色土丘陵区地形复杂,使得该区域的土壤采样活动花费更高。到目前为止,对紫色土丘陵区农田土壤取样单元的研究还很少,能应用于采样实践的取样单元更是鲜见。为此,本研究将数字土壤制图技术、组合优化算法和模糊坡位分类技术相结合提出一种分坡位确定取样单元的方法,并将该方法应用于重庆市江津区一面积约为2km2的典型紫色土丘陵区农田地块(母质、气候、作物类型和管理措施基本一致),确定出不同土壤属性(土壤pH、有机质、碱解氮、有效磷和速效钾)在不同坡位(山脊、坡肩、背坡、坡脚和沟谷)的取样单元,同时在另外一块与研究区具有相似环境条件的区域对确定的取样单元进行了验证。主要研究结果如下：
1.紫色土丘陵区农田土壤属性空间分布预测
相关分析表明土壤pH值、有机质含量和碱解氮含量与地形因子之间的相关性比较强,而土壤有效磷含量和速效钾含量与地形因子之间的相关性比较弱,说明该研究区中地形对土壤pH值、有机质含量和碱解氮含量的空间分布影响显著,而对土壤有效磷含量和速效钾含量影响比较弱。方差分析表明土壤pH值、有机质含量、碱解氮含量和有效磷含量在水田和旱地中的均值之间的差异达到了显著性水平,而土壤速效钾没有,说明土地利用方式对土壤pH值、有机质含量、碱解氮含量和有效磷含量的空间变异有显著性影响,而对土壤速效钾含量作用不明显。
构建的土壤有机质、碱解氮和pH预测模型能解释各自空间变异的较大部分,而土壤有效磷和速效钾预测模型的解释力则比较弱,这是因为磷和钾主要受母质的影响,而在本研究区内受地形因子的影响比较弱,这与前人的研究结果是一致的,预测结果也是可以接受的。
基于地形因子的土壤属性预测模型与基于地形因子和土地利用方式组合的土壤属性预测模型预测结果精度对比表明,在预测变量中加入土地利用类型不一定能提高模型的预测精度,其中,对土壤pH值的预测精度有所降低,对土壤碱解氮含量的预测精度几乎没有变化,对土壤有机质和有效磷含量的预测精度略有提高。这是因为在紫色土丘陵区,地形对土地利用方式布局影响很大,由此导致地形因子与土地利用类型对土壤属性的影响存在较大部分的交互作用,除去交互作用后土地利用方式对土壤属性变异解释能力非常微小。
2.利用模拟退火优化土壤样点布局和土壤—景观模型
本研究利用模拟退火算法结合多重线性回归对训练集中原始200个土壤样点的空间布局进行了系统优化,对5种土壤属性都给出了从2-199的样点布局优化组合,同时针对每一个样点组合还给出了与其对应的土壤—景观模型及预测误差(均方误差),从预测误差可以看出,对土壤pH值而言,最少只需6个优化的样点就可以替代原始200个采样点；对土壤有机质含量和碱解氮含量而言,最少都只需7个优化的样点就可以替代原始200个采样点；对土壤有效磷含量和速效钾含量而言,最少只需3个优化的样点就可以替代原始200个采样点。
从预测误差还可以看出,优化的采样点数分别为136、124、136、48和95时,模型预测土壤pH值、有机质含量、碱解氮含量、有效磷含量和速效钾含量空间分布的精度最高,将这些优化后的土壤—景观模型预测精度与原始200个样点构建的土壤—景观模型相比较会发现,优化后的土壤—景观模型的拟合度比原始土壤—景观模型都有提高,其中土壤有效磷和速效钾提高最明显,分别提高了263.82%和192.25%,其次为土壤pH值,提高了18.86%,土壤碱解氮也提高了8.38%,土壤有机质提高了4.56%；而预测误差和模型复杂度都有减小,对MAE而言,土壤pH值和有效磷降低明显,分别减少了11.83%和11.20%,土壤有机质、速效钾和碱解氮分别减少了3.99%、1.79%和1.63%；对RMSE而言,土壤有效磷和速效钾减少明显,分别降低了12.14%和11.06%,其次为土壤pH,降低了9.29%,土壤碱解氮减少了6.88%,土壤有机质减少了3.93%；对AIC而言,土壤pH、有效磷、速效钾、有机质和碱解氮分别降低了4.33%、2.78%、1.87%、1.39%和1.27%。
3.紫色土区田间尺度农地景观坡位划分
本研究运用基于相似度的模糊推理方法对紫色土丘陵区田间尺度下农地景观的一块复杂地形区域进行了坡位分类,通过分类图可以看出,坡位分类结果整体上符合研究区的实际地形地貌特征；为了进一步验证该方法在地形复杂区域中的可行性与可靠性,又分别将土壤物理(土层厚度)、土壤化学(土壤养分)、土壤发生属性(土壤类型)以及土地利用类型与坡位分类结果相结合,利用单因素方差分析比较了土壤养分在不同坡位之间的差异,同时,又利用关系图验证了土层厚度和土壤类型与坡位隶属度之间存在明显的关系,最后,又运用对应分析验证了土地利用类型在坡位上的分布状况,这些结果更加明确地验证了分类结果的合理性,表明基于相似度的模糊推理模型在紫色土丘陵区进行坡位划分是合理的和可行的。
与确定性坡位分类方法相比,基于相似度的模糊推理不但可以给出坡位的定性分类(“硬化”分类),而且还能对“硬化”分类的不确定性作出定量评估(最大相似度图),以及能定量描述每类坡位的渐变信息(坡位相似度图),这可以为精细尺度上的坡面侵蚀、预测性土壤制图等模拟过程提供详细的信息,有助于提高模型的预测精度。例如,本研究发现在“硬化”分类中确定性较大的区域(相似度[0.8,1]),土种的空间分布与确定性坡位有明显的关系,而在确定性较小的区域(相似度[0,0.5]),土种的空间分布与确定性坡位的关系不明显,表明在进行土壤-景观的研究时,坡位分类的不确定性是一个不可忽视的因素,否则可能会导致错误的结果或推断。
4.紫色土丘陵区农田土壤不同坡位取样单元的确定
利用地统计方法拟合了不同土壤属性在不同坡位和整个研究区的半方差模型,半方差模型中的变程参数可以反映土壤属性的变异程度,土壤属性在不同坡位上的变程各不相同,且差异明显,表明土壤属性在不同坡位中的变异性是有明显差异的,同时再将土壤属性在不同坡位上的变程与在整个研究区的变程相比,发现不同坡位上的变程都大于整个研究区中的变程,这表明土壤属性在不同坡位上的变异性要低于整个研究区。这些结果说明：(1)分坡位确定取样单元是合理的,若按照传统均一的取样单元进行取样就会造成某些坡位的土壤样点过密,而另外一些坡位的土壤样点可能不足；(2)分坡位取样可以降低土壤属性的空间变异性,提高土壤取样效率。
本研究结合模拟退火算法、多重线性回归和坡位分类在15%的均值误差条件下确定出研究区土壤属性在不同坡位上的取样单元,其中,土壤pH值在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、9.4、10.4、10.4和11.5ha,土壤有机质含量在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、7.5、5.8、6.2和5.7ha,土壤碱解氮含量在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、6.2、5.8、5.2和11.5ha,土壤有效磷在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为2.9、2.5、2.3、1.8和3.8ha,土壤速效钾在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为4.3、3.7、3.3、2.8和5.7ha。进一步我们将确定的土壤属性在不同坡位上的取样单元应用于一个与本研究区具有相似环境条件的区域,对其实用性进行验证,验证结果表明,在验证区除了土壤有效磷和速效钾在山脊处的精度达不到要求外,其他按照推荐的取样单元所取的土壤样点均能满足给定的精度要求,这说明我们所确定的取样单元在整体上能满足取样精度的要求,是合适的。
Soil sampling is essential for acquiring soil information and spatial distribution; however, sampling across an area is usually time-and labor-consuming as well as costly. It is desirable for a survey to collect the minimum number of soil samples necessary to estimate the values of soil properties within a specified area. In purple soil hilly region, southwestern China, the dry sloping soils developed on the identical parent material are generally planted with the same crop and under the uniform management practice; even so soil still exhibits evident heterogeneity at spatial space. The fact indicates that topography imposes an influential effect on soil properties, and the effect must be taken into consideration when establishing a sampling scheme. In addition, complex terrains of this region make it more difficult to collect soil samples compared with an area with flat terrain. Therefore, it is necessary to determine an appropriate sampling density to guide soil sampling in this area. A method of determining soil sampling density was developed by combining digital soil mapping, combinational optimizing algorithm, and slope classification. The proposed method was applied to a farmland area (covering an area of approximately2km2) located in Yongxing, Jiangjin, Chongqing (calibration area), which is representative of a purple soil hilly region; and sampling density of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) for slope positions (ridge, shoulder, backslope, footslope, and valley) was determined. Then the determined soil sampling density was used to select soil samples in another area (validation area) which is similar in environmental conditions with calibration area. The main results are as follows:
1. Spatial prediction of soil properties within a farmland in purple soil hilly region
Pearson correlation coefficients showed that soil pH, organic matter, and alkali-hydrolyzable N strongly correlated with terrain attributes, while the relations between soil available P, available K and topographical indicators were poor. This information indicated that topography markedly influenced variation of soil pH, organic matter, and alkali-hydrolyzable N; however, the effect of terrain attributes on soil available P, and available K were weak. One-way analysis of variance (ANOVA) found significant differences for soil pH, organic matter, alkali-hydrolyzable N, and available P with land use types (crop land and paddy field), while not for available K. The results revealed that land use types significantly affected variance of these soil properties (except available K).
Prediction models (based on terrain attributes and based on combination of terrain attributes and land use types) could account for3.1～72.4%of the variability in soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K). The models in predicting organic matter, alkali-hydrolyzable N, and pH had a good predictive ability, however, that of available P and available K prediction models was poor because of P and K were mainly controlled by parent materials while not by topography in this study area. The result was consistent with findings of previous researchers and could be accepted.
Comparisons between prediction models based on terrain attributes and based on combination of terrain attributes and land use types indicated that inclusion of land use types in the prediction models was not always improving the prediction accuracy. For example, the precision of the model based on combination of terrain attributes and land use types in predicting pH was poorer than that of the model based on terrain attributes; and for alkali-hydrolyzable N, the predictive ability of the two types of prediction models were nearly the same; there were only a little improvements in predicting soil organic matter and available P of inclusion of land use types in prediction models. It could be explained that topography determined the spatial distribution of land use types to a great extent in the current study area, thus topography and land use jointly influenced soil properties and their effects were mostly interactered. If the interactive effects were filtered out, the contribution of the land use types in explaining the variability of soil properties would be very limited and could be ignored.
2. Optimizing spatial distribution of soil sampling points and soil-landscape model using simulated annealing (SA) algorithm
Simulated annealing combined with multiple linear regression was used to optimize the spatial distribution of original200soil sampling points in the calibration data set. The original200soil sampling points were optimized in spatial distribution from2to199for each soil property. An optimized soil-landscape model and corresponding prediction error (mean squared error) were given to each combination of optimized soil sampling points. From the prediction error, it could be seen that6,7,7,3, and3optimized soil sampling points could be used to replace the original200soil sampling points to predict the spatial variation of pH, organic matter, alkali-hydrolyzable N, available P, and available K, respectively.
As also seen from the prediction errors, accuracies of the prediction models (for soil pH, organic matter, alkali-hydrolyzable N, available P, and available K, respectively) were the most high when the number of optimized soil sampling points was136,124,48, and95, respectively. Values of adjusted determination coefficient (R2adj) of optimized soil-landscape models were all larger than that of the original soil-landscape models. The values of R2adj of optimized soil-landscape models were improved by263.82,192.25,18.86,8.38, and4.56%for available P, available K, pH, alkali-hydrolyzable N, and organic matter, respectively, compared with those of the original soil-landscape models. On the contrary, prediction errors of the optimized soil-landscapes were all lower than those of the original soil-landscape models. Values of mean absolute error (MAE) of the optimized models were reduced by11.83,11.20,3.99,1.79, and1.63%for pH, available P, organic matter, available K, and alkali-hydrolyzable N, respectively, compared with those of the original soil-landscape models. Values of root mean squared error (RMSE) of the optimized models were reduced by12.14,11.06,9.29,6.88, and3.93%for available P, available K, pH, alkali-hydrolyzable N, and organic matter, respectively, compared with those of the original soil-landscape models. Values of akaike information criterion (AIC) of the optimized models were reduced by4.33,2.78,1.87,1.39, and1.27%for soil pH, available P, available K, organic matter, and alkali-hydrolyzable N, respectively, compared with those of the original soil-landscape models.
3. Field scale slope position segmentation at agricultural landscape in purple soil hilly region
Field scale slope positions segmentation was carried out by using similarity-based approach at agricultural landscape in purple soil hilly region. The classified slope positions (ridge, shoulder, backslope, footslope, and valley) generally followed the actual feature of the landform. To further validate the usefulness of the similarity-based model in complex terrain area, one-way ANOVA was applied to examine differences of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) among slope positions. The results of ANOVA showed that significant differences for soil properties were found with slope positions. In addition, the relationships between soil thickness, soil types and quantified spatial gradient were also investigated and obvious trends were found. Finally, correspondence analysis (CA) was employed to examine the relations between slope positions and spatial distribution of land use types. The results showed that topography controlled the spatial distribution of land use types. All these information verified the validity of similarity-based approach used in the complex terrain area.
Compared with the traditional slope position classification method, the similarity-based approach not only could produce the "harden" map of the slope positions, but also could quantitatively describe the spatial gradient of slope information. The latter could provide detailed information for simulation process, such as slope erosion or predictive soil mapping, at fine scale. Areas with little fuzziness correspond well to soil species, while areas with high ambiguity correspond to miscellaneous soil species at the transitional areas of slope positions. This indicated that uncertainties in slope position classification must be considered in soil-landscape modeling, otherwise it would lead to wrong inference.
4. Determination of soil sampling density for slope positions at agricultural landscape in purple soil hilly region
Geostatistical method was employed to fit semi variogram of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) for slope positions (ridge, shoulder, backslope, footslope, and valley) and the whole study area. The parameter range of the semi variogram could reflect the degree of soil variation. The larger a range the less variable a soil property would be, and vice versa. The ranges of soil properties for slope positions were different from each other, which indicated that variation of soil properties among slope positions were not the same. This verified the justifiability of the proposed method determining soil sampling density for slope positions. Further, ranges of soil properties for slope positions were all larger than that of the whole study area. This showed that soil properties variability was more homogeneous within slope positions than that of the whole study area, which implied that slope position segmentation could improve efficiency in soil sampling.
The proposed method combining simulated annealing, multiple linear regression, and slope position classification was used to determine soil sampling density for slope positions (ridge, shoulder, backsloe, footslope, and valley) with15%proper relative error. The sampling densities of pH were8.6,9.4,10.4,10.4, and11.5ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil organic matter were8.6,7.5,5.8,6.2, and5.7ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil alkali-hydrolyzable N were8.6,6.2,5.8,5.2, and11.5ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil available P were2.9,2.5,2.3,1.8, and3.8ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil available K were4.3,3.7,3.3,2.8, and5.7ha for ridge, shoulder, backslope, footslope, and valley, respectively. Further, the determined soil sampling density was applied to another area which was similar in environmental conditions with the calibration area. The results showed that the determined soil sampling density could meet the given accuracy requirement.
1.紫色土丘陵区农田土壤属性空间分布预测
相关分析表明土壤pH值、有机质含量和碱解氮含量与地形因子之间的相关性比较强,而土壤有效磷含量和速效钾含量与地形因子之间的相关性比较弱,说明该研究区中地形对土壤pH值、有机质含量和碱解氮含量的空间分布影响显著,而对土壤有效磷含量和速效钾含量影响比较弱。方差分析表明土壤pH值、有机质含量、碱解氮含量和有效磷含量在水田和旱地中的均值之间的差异达到了显著性水平,而土壤速效钾没有,说明土地利用方式对土壤pH值、有机质含量、碱解氮含量和有效磷含量的空间变异有显著性影响,而对土壤速效钾含量作用不明显。
构建的土壤有机质、碱解氮和pH预测模型能解释各自空间变异的较大部分,而土壤有效磷和速效钾预测模型的解释力则比较弱,这是因为磷和钾主要受母质的影响,而在本研究区内受地形因子的影响比较弱,这与前人的研究结果是一致的,预测结果也是可以接受的。
基于地形因子的土壤属性预测模型与基于地形因子和土地利用方式组合的土壤属性预测模型预测结果精度对比表明,在预测变量中加入土地利用类型不一定能提高模型的预测精度,其中,对土壤pH值的预测精度有所降低,对土壤碱解氮含量的预测精度几乎没有变化,对土壤有机质和有效磷含量的预测精度略有提高。这是因为在紫色土丘陵区,地形对土地利用方式布局影响很大,由此导致地形因子与土地利用类型对土壤属性的影响存在较大部分的交互作用,除去交互作用后土地利用方式对土壤属性变异解释能力非常微小。
2.利用模拟退火优化土壤样点布局和土壤—景观模型
本研究利用模拟退火算法结合多重线性回归对训练集中原始200个土壤样点的空间布局进行了系统优化,对5种土壤属性都给出了从2-199的样点布局优化组合,同时针对每一个样点组合还给出了与其对应的土壤—景观模型及预测误差(均方误差),从预测误差可以看出,对土壤pH值而言,最少只需6个优化的样点就可以替代原始200个采样点；对土壤有机质含量和碱解氮含量而言,最少都只需7个优化的样点就可以替代原始200个采样点；对土壤有效磷含量和速效钾含量而言,最少只需3个优化的样点就可以替代原始200个采样点。
从预测误差还可以看出,优化的采样点数分别为136、124、136、48和95时,模型预测土壤pH值、有机质含量、碱解氮含量、有效磷含量和速效钾含量空间分布的精度最高,将这些优化后的土壤—景观模型预测精度与原始200个样点构建的土壤—景观模型相比较会发现,优化后的土壤—景观模型的拟合度比原始土壤—景观模型都有提高,其中土壤有效磷和速效钾提高最明显,分别提高了263.82%和192.25%,其次为土壤pH值,提高了18.86%,土壤碱解氮也提高了8.38%,土壤有机质提高了4.56%；而预测误差和模型复杂度都有减小,对MAE而言,土壤pH值和有效磷降低明显,分别减少了11.83%和11.20%,土壤有机质、速效钾和碱解氮分别减少了3.99%、1.79%和1.63%；对RMSE而言,土壤有效磷和速效钾减少明显,分别降低了12.14%和11.06%,其次为土壤pH,降低了9.29%,土壤碱解氮减少了6.88%,土壤有机质减少了3.93%；对AIC而言,土壤pH、有效磷、速效钾、有机质和碱解氮分别降低了4.33%、2.78%、1.87%、1.39%和1.27%。
3.紫色土区田间尺度农地景观坡位划分
本研究运用基于相似度的模糊推理方法对紫色土丘陵区田间尺度下农地景观的一块复杂地形区域进行了坡位分类,通过分类图可以看出,坡位分类结果整体上符合研究区的实际地形地貌特征；为了进一步验证该方法在地形复杂区域中的可行性与可靠性,又分别将土壤物理(土层厚度)、土壤化学(土壤养分)、土壤发生属性(土壤类型)以及土地利用类型与坡位分类结果相结合,利用单因素方差分析比较了土壤养分在不同坡位之间的差异,同时,又利用关系图验证了土层厚度和土壤类型与坡位隶属度之间存在明显的关系,最后,又运用对应分析验证了土地利用类型在坡位上的分布状况,这些结果更加明确地验证了分类结果的合理性,表明基于相似度的模糊推理模型在紫色土丘陵区进行坡位划分是合理的和可行的。
与确定性坡位分类方法相比,基于相似度的模糊推理不但可以给出坡位的定性分类(“硬化”分类),而且还能对“硬化”分类的不确定性作出定量评估(最大相似度图),以及能定量描述每类坡位的渐变信息(坡位相似度图),这可以为精细尺度上的坡面侵蚀、预测性土壤制图等模拟过程提供详细的信息,有助于提高模型的预测精度。例如,本研究发现在“硬化”分类中确定性较大的区域(相似度[0.8,1]),土种的空间分布与确定性坡位有明显的关系,而在确定性较小的区域(相似度[0,0.5]),土种的空间分布与确定性坡位的关系不明显,表明在进行土壤-景观的研究时,坡位分类的不确定性是一个不可忽视的因素,否则可能会导致错误的结果或推断。
4.紫色土丘陵区农田土壤不同坡位取样单元的确定
利用地统计方法拟合了不同土壤属性在不同坡位和整个研究区的半方差模型,半方差模型中的变程参数可以反映土壤属性的变异程度,土壤属性在不同坡位上的变程各不相同,且差异明显,表明土壤属性在不同坡位中的变异性是有明显差异的,同时再将土壤属性在不同坡位上的变程与在整个研究区的变程相比,发现不同坡位上的变程都大于整个研究区中的变程,这表明土壤属性在不同坡位上的变异性要低于整个研究区。这些结果说明：(1)分坡位确定取样单元是合理的,若按照传统均一的取样单元进行取样就会造成某些坡位的土壤样点过密,而另外一些坡位的土壤样点可能不足；(2)分坡位取样可以降低土壤属性的空间变异性,提高土壤取样效率。
本研究结合模拟退火算法、多重线性回归和坡位分类在15%的均值误差条件下确定出研究区土壤属性在不同坡位上的取样单元,其中,土壤pH值在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、9.4、10.4、10.4和11.5ha,土壤有机质含量在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、7.5、5.8、6.2和5.7ha,土壤碱解氮含量在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为8.6、6.2、5.8、5.2和11.5ha,土壤有效磷在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为2.9、2.5、2.3、1.8和3.8ha,土壤速效钾在山脊、坡肩、背坡、坡脚和沟谷的取样单元分别为4.3、3.7、3.3、2.8和5.7ha。进一步我们将确定的土壤属性在不同坡位上的取样单元应用于一个与本研究区具有相似环境条件的区域,对其实用性进行验证,验证结果表明,在验证区除了土壤有效磷和速效钾在山脊处的精度达不到要求外,其他按照推荐的取样单元所取的土壤样点均能满足给定的精度要求,这说明我们所确定的取样单元在整体上能满足取样精度的要求,是合适的。
Soil sampling is essential for acquiring soil information and spatial distribution; however, sampling across an area is usually time-and labor-consuming as well as costly. It is desirable for a survey to collect the minimum number of soil samples necessary to estimate the values of soil properties within a specified area. In purple soil hilly region, southwestern China, the dry sloping soils developed on the identical parent material are generally planted with the same crop and under the uniform management practice; even so soil still exhibits evident heterogeneity at spatial space. The fact indicates that topography imposes an influential effect on soil properties, and the effect must be taken into consideration when establishing a sampling scheme. In addition, complex terrains of this region make it more difficult to collect soil samples compared with an area with flat terrain. Therefore, it is necessary to determine an appropriate sampling density to guide soil sampling in this area. A method of determining soil sampling density was developed by combining digital soil mapping, combinational optimizing algorithm, and slope classification. The proposed method was applied to a farmland area (covering an area of approximately2km2) located in Yongxing, Jiangjin, Chongqing (calibration area), which is representative of a purple soil hilly region; and sampling density of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) for slope positions (ridge, shoulder, backslope, footslope, and valley) was determined. Then the determined soil sampling density was used to select soil samples in another area (validation area) which is similar in environmental conditions with calibration area. The main results are as follows:
1. Spatial prediction of soil properties within a farmland in purple soil hilly region
Pearson correlation coefficients showed that soil pH, organic matter, and alkali-hydrolyzable N strongly correlated with terrain attributes, while the relations between soil available P, available K and topographical indicators were poor. This information indicated that topography markedly influenced variation of soil pH, organic matter, and alkali-hydrolyzable N; however, the effect of terrain attributes on soil available P, and available K were weak. One-way analysis of variance (ANOVA) found significant differences for soil pH, organic matter, alkali-hydrolyzable N, and available P with land use types (crop land and paddy field), while not for available K. The results revealed that land use types significantly affected variance of these soil properties (except available K).
Prediction models (based on terrain attributes and based on combination of terrain attributes and land use types) could account for3.1～72.4%of the variability in soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K). The models in predicting organic matter, alkali-hydrolyzable N, and pH had a good predictive ability, however, that of available P and available K prediction models was poor because of P and K were mainly controlled by parent materials while not by topography in this study area. The result was consistent with findings of previous researchers and could be accepted.
Comparisons between prediction models based on terrain attributes and based on combination of terrain attributes and land use types indicated that inclusion of land use types in the prediction models was not always improving the prediction accuracy. For example, the precision of the model based on combination of terrain attributes and land use types in predicting pH was poorer than that of the model based on terrain attributes; and for alkali-hydrolyzable N, the predictive ability of the two types of prediction models were nearly the same; there were only a little improvements in predicting soil organic matter and available P of inclusion of land use types in prediction models. It could be explained that topography determined the spatial distribution of land use types to a great extent in the current study area, thus topography and land use jointly influenced soil properties and their effects were mostly interactered. If the interactive effects were filtered out, the contribution of the land use types in explaining the variability of soil properties would be very limited and could be ignored.
2. Optimizing spatial distribution of soil sampling points and soil-landscape model using simulated annealing (SA) algorithm
Simulated annealing combined with multiple linear regression was used to optimize the spatial distribution of original200soil sampling points in the calibration data set. The original200soil sampling points were optimized in spatial distribution from2to199for each soil property. An optimized soil-landscape model and corresponding prediction error (mean squared error) were given to each combination of optimized soil sampling points. From the prediction error, it could be seen that6,7,7,3, and3optimized soil sampling points could be used to replace the original200soil sampling points to predict the spatial variation of pH, organic matter, alkali-hydrolyzable N, available P, and available K, respectively.
As also seen from the prediction errors, accuracies of the prediction models (for soil pH, organic matter, alkali-hydrolyzable N, available P, and available K, respectively) were the most high when the number of optimized soil sampling points was136,124,48, and95, respectively. Values of adjusted determination coefficient (R2adj) of optimized soil-landscape models were all larger than that of the original soil-landscape models. The values of R2adj of optimized soil-landscape models were improved by263.82,192.25,18.86,8.38, and4.56%for available P, available K, pH, alkali-hydrolyzable N, and organic matter, respectively, compared with those of the original soil-landscape models. On the contrary, prediction errors of the optimized soil-landscapes were all lower than those of the original soil-landscape models. Values of mean absolute error (MAE) of the optimized models were reduced by11.83,11.20,3.99,1.79, and1.63%for pH, available P, organic matter, available K, and alkali-hydrolyzable N, respectively, compared with those of the original soil-landscape models. Values of root mean squared error (RMSE) of the optimized models were reduced by12.14,11.06,9.29,6.88, and3.93%for available P, available K, pH, alkali-hydrolyzable N, and organic matter, respectively, compared with those of the original soil-landscape models. Values of akaike information criterion (AIC) of the optimized models were reduced by4.33,2.78,1.87,1.39, and1.27%for soil pH, available P, available K, organic matter, and alkali-hydrolyzable N, respectively, compared with those of the original soil-landscape models.
3. Field scale slope position segmentation at agricultural landscape in purple soil hilly region
Field scale slope positions segmentation was carried out by using similarity-based approach at agricultural landscape in purple soil hilly region. The classified slope positions (ridge, shoulder, backslope, footslope, and valley) generally followed the actual feature of the landform. To further validate the usefulness of the similarity-based model in complex terrain area, one-way ANOVA was applied to examine differences of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) among slope positions. The results of ANOVA showed that significant differences for soil properties were found with slope positions. In addition, the relationships between soil thickness, soil types and quantified spatial gradient were also investigated and obvious trends were found. Finally, correspondence analysis (CA) was employed to examine the relations between slope positions and spatial distribution of land use types. The results showed that topography controlled the spatial distribution of land use types. All these information verified the validity of similarity-based approach used in the complex terrain area.
Compared with the traditional slope position classification method, the similarity-based approach not only could produce the "harden" map of the slope positions, but also could quantitatively describe the spatial gradient of slope information. The latter could provide detailed information for simulation process, such as slope erosion or predictive soil mapping, at fine scale. Areas with little fuzziness correspond well to soil species, while areas with high ambiguity correspond to miscellaneous soil species at the transitional areas of slope positions. This indicated that uncertainties in slope position classification must be considered in soil-landscape modeling, otherwise it would lead to wrong inference.
4. Determination of soil sampling density for slope positions at agricultural landscape in purple soil hilly region
Geostatistical method was employed to fit semi variogram of soil properties (soil pH, organic matter, alkali-hydrolyzable N, available P, and available K) for slope positions (ridge, shoulder, backslope, footslope, and valley) and the whole study area. The parameter range of the semi variogram could reflect the degree of soil variation. The larger a range the less variable a soil property would be, and vice versa. The ranges of soil properties for slope positions were different from each other, which indicated that variation of soil properties among slope positions were not the same. This verified the justifiability of the proposed method determining soil sampling density for slope positions. Further, ranges of soil properties for slope positions were all larger than that of the whole study area. This showed that soil properties variability was more homogeneous within slope positions than that of the whole study area, which implied that slope position segmentation could improve efficiency in soil sampling.
The proposed method combining simulated annealing, multiple linear regression, and slope position classification was used to determine soil sampling density for slope positions (ridge, shoulder, backsloe, footslope, and valley) with15%proper relative error. The sampling densities of pH were8.6,9.4,10.4,10.4, and11.5ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil organic matter were8.6,7.5,5.8,6.2, and5.7ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil alkali-hydrolyzable N were8.6,6.2,5.8,5.2, and11.5ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil available P were2.9,2.5,2.3,1.8, and3.8ha for ridge, shoulder, backslope, footslope, and valley, respectively. The sampling densities of soil available K were4.3,3.7,3.3,2.8, and5.7ha for ridge, shoulder, backslope, footslope, and valley, respectively. Further, the determined soil sampling density was applied to another area which was similar in environmental conditions with the calibration area. The results showed that the determined soil sampling density could meet the given accuracy requirement.
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