Simulation of net primary production in a grassland and subsequent calculations of belowground net production revealed substantial sources of error that may be common to all estimates of belowground net production from field data. Inherent bias in the most frequently used estimators and a large counterintuitive effect of sample variability require that techniques be carefully considered before conclusions are drawn based upon estimates of belowground net production
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
The ocular estimate by plot method may be biased by the lack of proper weighting procedures. The nat...
This paper addresses the issue of the effect of random errors in field estimates of net primary prod...
A simulation model which reflected the seasonal dynamics of biomass in a North America mixed prairie...
Net primary production (NPP) is a fundamental characteristic of all ecosystems and foundational to u...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) for the com...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) of SOC stoc...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) of SOC stoc...
In many grasslands, aboveground net primary productivity (ANPP) is commonly estimated by measuring p...
Belowground production in the shortgrass steppe is important because it can represent 80% of the bio...
The Journal of Agricultural Science, Cambridge has been a fixture in dissemination of crop simulatio...
Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-n...
Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical ...
Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three...
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
The ocular estimate by plot method may be biased by the lack of proper weighting procedures. The nat...
This paper addresses the issue of the effect of random errors in field estimates of net primary prod...
A simulation model which reflected the seasonal dynamics of biomass in a North America mixed prairie...
Net primary production (NPP) is a fundamental characteristic of all ecosystems and foundational to u...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) for the com...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) of SOC stoc...
Root mean square error (RMSE) and mean difference of simulations and observations (BIAS) of SOC stoc...
In many grasslands, aboveground net primary productivity (ANPP) is commonly estimated by measuring p...
Belowground production in the shortgrass steppe is important because it can represent 80% of the bio...
The Journal of Agricultural Science, Cambridge has been a fixture in dissemination of crop simulatio...
Multi-model ensembles are becoming increasingly accepted for the estimation of agricultural carbon-n...
Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical ...
Uncertainty in above ground forest biomass (AGB) estimates at broad-scale depends primarily on three...
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
We show the error in water-limited yields simulated by crop models which is associated with spatiall...
The ocular estimate by plot method may be biased by the lack of proper weighting procedures. The nat...