Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the ...Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the loss of soil-organic-carbon (SOC), which further enhances soil fertility. Different fractions of SOC pools react to the alterations in management practices and indicate changes in SOC dynamics as compared to total C in the soil. Higher SOC levels in soil have been observed in case of reduced/no-till (NT) practices than conventional tillage (CT). However, between CT and zero tillage/NT, total SOC stocks diminished with an increase in soil depth, which demonstrated that the benefits of SOC are more pronounced in the topsoil under NT. Soil aggregation provides physical protection to C associated with different-sized particles, thus, the improvement in soil aggregation through CA is an effective way to mitigate soil C loss. Along with less soil disturbance, residual management, suitable crop rotation, rational application of manures and fertilizers, and integrated nutrient management have been found to be effective in not only improving soil C stock but also enhancing the soil health and productivity. Thus, CA can be considered as a potential method in the build-up of SOC of soil in rice-wheat system.展开更多
基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensembl...基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。展开更多
文摘Ploughing and fertilization practices in rice-wheat system have deteriorated the soil carbon (C) pools. Conservation agriculture (CA) based management approaches have proven to enhance C sequestration and reverse the loss of soil-organic-carbon (SOC), which further enhances soil fertility. Different fractions of SOC pools react to the alterations in management practices and indicate changes in SOC dynamics as compared to total C in the soil. Higher SOC levels in soil have been observed in case of reduced/no-till (NT) practices than conventional tillage (CT). However, between CT and zero tillage/NT, total SOC stocks diminished with an increase in soil depth, which demonstrated that the benefits of SOC are more pronounced in the topsoil under NT. Soil aggregation provides physical protection to C associated with different-sized particles, thus, the improvement in soil aggregation through CA is an effective way to mitigate soil C loss. Along with less soil disturbance, residual management, suitable crop rotation, rational application of manures and fertilizers, and integrated nutrient management have been found to be effective in not only improving soil C stock but also enhancing the soil health and productivity. Thus, CA can be considered as a potential method in the build-up of SOC of soil in rice-wheat system.
文摘基于观测数据和作物模型相同化的田块尺度作物生长监测,对于农田精准管理具有重要意义。为构建能准确模拟旱区春小麦长势和产量的同化模拟模型,该研究利用SWAP(soil-water-atmosphere-plant)模型和迭代集合平滑器算法(iterative ensemble smoother,IES),构建了适合旱区春小麦的SWAP-IES同化模拟系统,并利用2019—2020年田间观测试验数据,评估了同化叶面积指数(leaf area index,LAI)、土壤水分(soil water content,SW)及其组合在旱区春小麦生长模拟和估产中的作用。结果表明,相较于无同化情景,在吸收6次土壤水分观测数据后,模型对土壤水分模拟的R^(2)从0.48提升到0.87。同化LAI时,各水分胁迫处理下LAI的模拟精度均最高,R^(2)从无同化的0.35~0.62提升到0.76~0.96。同化LAI+SW时,各处理对生物量模拟的精度均最高,R^(2)从无同化的0.40~0.67提升到0.73~0.96。轻度水分胁迫处理(T4~T5)下,仅同化LAI即可达到较好的估产效果,相对误差为4.05%~9.17%,而在中度或重度水分胁迫处理(T1~T3)下,准确的产量估算需同时吸收LAI和SW,相对误差为3.87%~8.38%。开花期和拔节期的观测数据对提高SWAP-IES系统估产精度的作用最大,同时吸收开花期和拔节期LAI+SW观测数据时估产的R^(2)可从无同化的0.45提高到0.79。说明所构建的SWAP-IES同化模拟系统,在融入开花期和拔节期等关键生育期的观测数据后能有效模拟不同水分处理下春小麦生长和产量形成过程,可为田块尺度下旱区春小麦精准监测提供技术参考。
