Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r...Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.展开更多
Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories es...Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.展开更多
Background:Non-timber forest products(NTFPs)are an important part of forest biodiversity,and the subsistence and trade of local people,especially in less developed countries.Because of the high ecological and economic...Background:Non-timber forest products(NTFPs)are an important part of forest biodiversity,and the subsistence and trade of local people,especially in less developed countries.Because of the high ecological and economic value,NTFPs have faced the problem of over-exploitation,and the key to solve this problem is to determine the feasible way of sustainable utilization of NTFPs.Harvest intensity is one of the most important and easily controlled utilization factors,which can greatly influence the plant individual survival,growth and reproductive performances,and even the population structure and dynamics.Therefore,we chose two common and important NTFPs species with different marketable parts(i.e.,Acanthopanax senticosus with tender leaves and Aralia elata with tender buds)as our study objects.Aiming to determine the optimum harvest intensity for sustainably utilizing both NTFPs species,five levels of harvest intensity treatments(i.e.,control,light,medium,high and severe)were designed to assess the effects of harvest intensity on their marketable organ yield,plant growth and reproductive performances.Results:The biomass growth rates of marketable organ and plant growth of A.senticosus under light harvest intensity treatment were significantly higher than those under other harvest intensities.The plant height growth and 1000-seed weight of A.elata under severe harvest intensity treatment were significantly lower than those under control treatment.Conclusions:The light harvest intensity with 25% leaf removal and the high harvest intensity with all terminal buds harvested are the optimum harvest intensity to maintain the sustainable utilization of A.senticosus and A.elata,respectively.These findings could provide managers with basic but practical guidance for making decisions about the sustainable harvest management plan for the cultivated NTFPs species,and further provide a theoretical basis for managers to establish the harvest regulations for wild NTFPs species.Consequently,the local residents or communities can improve their income while ensure the sustainable development of wild NTFPs.展开更多
Effects of environmental factors such as climate,topography,vegetation and soil in shelter forests in Three Gorges Reservoir Region on runoff and sediment yields were monitored to identify dominant environmental facto...Effects of environmental factors such as climate,topography,vegetation and soil in shelter forests in Three Gorges Reservoir Region on runoff and sediment yields were monitored to identify dominant environmental factors controlling runoff and sediment yields in 15 runoff plots in study area by soil sampling,laboratory analysis,stepwise regression analysis and path analysis,and to establish the main control environmental factors that affect runoff and sediment yields. The results showed that soil bulk density,herbaceous cover,slope,and canopy density were the significant factors controlling runoff,and the direct path coefficient of each factor was ranked as canopy closure(-0. 628) > litter thickness(-0. 547) > bulk density( 0. 509) > altitude( 0. 289). The indirect path coefficient was ranked as soil bulk density( 0. 354) >litter thickness(-0. 169) > altitude( 0. 126) > canopy closure(-0. 104). Therefore,canopy closure and litter thickness mainly had direct effects on runoff,while soil bulk density mainly had indirect effects through their contributions to other factors. Herbaceous cover,litter thickness,slope,canopy density,and altitude were the significant factors controlling sediment yields. The direct path coefficient of each factor was ranked as herbaceous cover(-0. 815) > litter thickness(-0. 777) > canopy closure(-0. 624) > slope( 0. 620). The indirect path coefficient was ranked as slope( 0. 272) > litter thickness(-0. 131) > canopy closure(-0. 097) > herbaceous cover(-0. 084). Therefore,herbaceous cover and litter thickness mainly had direct effects on sediment yields,while slope mainly had indirect effects through their contributions to other factors. All the selected environmental factors jointly explained 85. 5% and 78. 3% of runoff and sediment yield variability,respectively. However,there were large values of remaining path coefficients of other factors influencing runoff and sediment yields,which indicated that some important factors are not included and should be taken into account.展开更多
Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applic...Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.52079103)。
文摘Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.
文摘Forest management planning often relies on Airborne Laser Scanning(ALS)-based Forest Management Inventories(FMIs)for sustainable and efficient decision-making.Employing the area-based(ABA)approach,these inventories estimate forest characteristics for grid cell areas(pixels),which are then usually summarized at the stand level.Using the ALS-based high-resolution Norwegian Forest Resource Maps(16 m×16 m pixel resolution)alongside with stand-level growth and yield models,this study explores the impact of three levels of pixel aggregation(standlevel,stand-level with species strata,and pixel-level)on projected stand development.The results indicate significant differences in the projected outputs based on the aggregation level.Notably,the most substantial difference in estimated volume occurred between stand-level and pixel-level aggregation,ranging from-301 to+253 m^(3)·ha^(-1)for single stands.The differences were,on average,higher for broadleaves than for spruce and pine dominated stands,and for mixed stands and stands with higher variability than for pure and homogenous stands.In conclusion,this research underscores the critical role of input data resolution in forest planning and management,emphasizing the need for improved data collection practices to ensure sustainable forest management.
基金supported by grants from the National Key R&D Program of China(2016YFC0500302)the National Natural Science Foundation of China(U1808201)+1 种基金Strategic Leading Science&Technology Programme,CAS(XDA23070100)the Liaoning Revitalization Talents Program(XLYC1807102).
文摘Background:Non-timber forest products(NTFPs)are an important part of forest biodiversity,and the subsistence and trade of local people,especially in less developed countries.Because of the high ecological and economic value,NTFPs have faced the problem of over-exploitation,and the key to solve this problem is to determine the feasible way of sustainable utilization of NTFPs.Harvest intensity is one of the most important and easily controlled utilization factors,which can greatly influence the plant individual survival,growth and reproductive performances,and even the population structure and dynamics.Therefore,we chose two common and important NTFPs species with different marketable parts(i.e.,Acanthopanax senticosus with tender leaves and Aralia elata with tender buds)as our study objects.Aiming to determine the optimum harvest intensity for sustainably utilizing both NTFPs species,five levels of harvest intensity treatments(i.e.,control,light,medium,high and severe)were designed to assess the effects of harvest intensity on their marketable organ yield,plant growth and reproductive performances.Results:The biomass growth rates of marketable organ and plant growth of A.senticosus under light harvest intensity treatment were significantly higher than those under other harvest intensities.The plant height growth and 1000-seed weight of A.elata under severe harvest intensity treatment were significantly lower than those under control treatment.Conclusions:The light harvest intensity with 25% leaf removal and the high harvest intensity with all terminal buds harvested are the optimum harvest intensity to maintain the sustainable utilization of A.senticosus and A.elata,respectively.These findings could provide managers with basic but practical guidance for making decisions about the sustainable harvest management plan for the cultivated NTFPs species,and further provide a theoretical basis for managers to establish the harvest regulations for wild NTFPs species.Consequently,the local residents or communities can improve their income while ensure the sustainable development of wild NTFPs.
基金Supported by National Key Technology Research and Development Program of the Ministry of Science and Technology of China(2015BAD07B04)Key Science and Technology Program of Henan Province,China(152102110059)
文摘Effects of environmental factors such as climate,topography,vegetation and soil in shelter forests in Three Gorges Reservoir Region on runoff and sediment yields were monitored to identify dominant environmental factors controlling runoff and sediment yields in 15 runoff plots in study area by soil sampling,laboratory analysis,stepwise regression analysis and path analysis,and to establish the main control environmental factors that affect runoff and sediment yields. The results showed that soil bulk density,herbaceous cover,slope,and canopy density were the significant factors controlling runoff,and the direct path coefficient of each factor was ranked as canopy closure(-0. 628) > litter thickness(-0. 547) > bulk density( 0. 509) > altitude( 0. 289). The indirect path coefficient was ranked as soil bulk density( 0. 354) >litter thickness(-0. 169) > altitude( 0. 126) > canopy closure(-0. 104). Therefore,canopy closure and litter thickness mainly had direct effects on runoff,while soil bulk density mainly had indirect effects through their contributions to other factors. Herbaceous cover,litter thickness,slope,canopy density,and altitude were the significant factors controlling sediment yields. The direct path coefficient of each factor was ranked as herbaceous cover(-0. 815) > litter thickness(-0. 777) > canopy closure(-0. 624) > slope( 0. 620). The indirect path coefficient was ranked as slope( 0. 272) > litter thickness(-0. 131) > canopy closure(-0. 097) > herbaceous cover(-0. 084). Therefore,herbaceous cover and litter thickness mainly had direct effects on sediment yields,while slope mainly had indirect effects through their contributions to other factors. All the selected environmental factors jointly explained 85. 5% and 78. 3% of runoff and sediment yield variability,respectively. However,there were large values of remaining path coefficients of other factors influencing runoff and sediment yields,which indicated that some important factors are not included and should be taken into account.
基金supported by the Department of Environmental Science,Urmia Lake Research Institute,Urmia University
文摘Developing regional models using physiographic, climatic, and hydrologic variables is an approach to estimating suspended load yield(SLY)in ungauged watersheds. However, using all the variables might reduce the applicability of these models. Therefore, data reduction techniques(DRTs), e.g., principal component analysis(PCA), Gamma test(GT), and stepwise regression(SR), have been used to select the most effective variables. The artificial neural network(ANN) and multiple linear regression(MLR) are also common tools for SLY modeling. We conducted this study(1) to obtain the most effective variables influencing SLY through DRTs including PCA, GT, and SR, and then, to use them as input data for ANN and MLR; and(2) to provide the best SLY models. Accordingly, we used 14 physiographic, climatic, and hydrologic parameters from 42 watersheds in the Hyrcanian forest region(in northern Iran). The most effective variables as determined through DRTs as well as the original data sets were used as the input data for ANN and MLR in order to provide an SLY model. The results indicated that the SLY models provided by ANN performed much better than the MLR models, and the GT-ANN model was the best. The determination of coefficient,relative error, root mean square error, and bias were 99.9%, 26%, 323 t/year, and 6 t/year in the calibration period, and 70%, 43%, 456 t/year, and 407 t/year in the validation period, respectively. Overall, selecting the main factors that influence SLY and using artificial intelligence tools can be useful for water resources managers to quickly determine the behavior of SLY in ungauged watersheds.
