scholarly journals ESTIMATE OF STAND DENSITY INDEX FOR EUCALYPTUS UROPHYLLA USING DIFFERENT FIT METHODS

2016 ◽  
Vol 40 (5) ◽  
pp. 921-929 ◽  
Author(s):  
Ernani Lopes Possato ◽  
Natalino Calegario ◽  
Gilciano Saraiva Nogueira ◽  
Elliezer de Almeida Melo ◽  
Joyce de Almeida Alves
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Genetic Material ◽  
Stand Density ◽  
Model Parameters ◽  
Eucalyptus Urophylla ◽  
Frontier Analysis ◽  
Density Index ◽  
Number Of Stems ◽  
Stand Density Index

ABSTRACT The Reineke stand density index (SDI) was created on 1933 and remains as target of researches due to its importance on helping decision making regarding the management of population density. Part of such works is focused on the manner by which plots were selected and methods for the fit of Reineke model parameters in order to improve the definition of SDI value for the genetic material evaluated. The present study aimed to estimate the SDI value for Eucalyptus urophylla using the Reineke model fitted by the method of linear regression (LR) and stochastic frontier analysis (SFA). The database containing pairs of data number of stems per hectare (N) and mean quadratic diameter (Dq) was selected in three intensities, containing the 8, 30 and 43 plots of greatest density, and models were fitted by LR and SFA on each selected intensities. The intensity of data selection altered slightly the estimates of parameters and SDI when comparing the fits of each method. On the other hand, the adjust method influenced the mean estimated values of slope and SDI, which corresponded to -1.863 and 740 for LR and -1.582 and 810 for SFA.

scholarly journals Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis

Econometrics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 13 ◽  
Author(s):  
Kamil Makieła ◽  
Błażej Mazur
Keyword(s):  
Latent Variables ◽  
Bayesian Model ◽  
Stochastic Frontier Analysis ◽  
Bayesian Model Averaging ◽  
Scale Parameter ◽  
Stochastic Frontier ◽  
Model Averaging ◽  
Model Parameters ◽  
Frontier Analysis ◽  
Stochastic Parameters

This paper discusses Bayesian model averaging (BMA) in Stochastic Frontier Analysis and investigates inference sensitivity to prior assumptions made about the scale parameter of (in)efficiency. We turn our attention to the “standard” prior specifications for the popular normal-half-normal and normal-exponential models. To facilitate formal model comparison, we propose a model that nests both sampling models and generalizes the symmetric term of the compound error. Within this setup it is possible to develop coherent priors for model parameters in an explicit way. We analyze sensitivity of different prior specifications on the aforementioned scale parameter with respect to posterior characteristics of technology, stochastic parameters, latent variables and—especially—the models’ posterior probabilities, which are crucial for adequate inference pooling. We find that using incoherent priors on the scale parameter of inefficiency has (i) virtually no impact on the technology parameters; (ii) some impact on inference about the stochastic parameters and latent variables and (iii) substantial impact on marginal data densities, which are crucial in BMA.

A species-specific, site-sensitive maximum stand density index model for Pacific Northwest conifer forests

2021 ◽  
pp. 1-12
Author(s):  
Ryan R. Heiderman ◽  
Mark J. Kimsey
Keyword(s):  
Species Composition ◽  
Pacific Northwest ◽  
Stochastic Frontier ◽  
Stand Density ◽  
Tsuga Heterophylla ◽  
The Self ◽  
Species Selection ◽  
Federal State ◽  
Density Index ◽  
Stand Density Index

Maximum stand density index (SDIMAX) models were developed for important Pacific Northwest conifers of western Oregon and Washington, USA, based on site and species influences and interactions. Inventory and monitoring data from numerous federal, state, and private forest management groups were obtained throughout the region to ensure a wide coverage of site characteristics. These observations include information on tree size, number, and species composition. The effects and influence on the self-thinning frontier of plot-specific factors such as climate, topography, soils, and geology, as well as species composition, were evaluated based on geographic location using a multistep approach to analysis involving linear quantile mixed models, random forest, and stochastic frontier functions. The self-thinning slope of forest stands dominated by Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) was found to be –1.517 and that of stands dominated by western hemlock (Tsuga heterophylla (Raf.) Sarg.) was found to be –1.461, leading to regionwide modelled SDIMAX values at the 95th percentile of 1728 and 1952 trees per hectare, respectively. The regional model of site-specific SDIMAX will support forest managers in decision-making regarding density management and species selection to more efficiently utilize site resources toward healthy, productive forests.

scholarly journals Wheat yield gaps across smallholder farming systems in Ethiopia

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
João Vasco Silva ◽  
Pytrik Reidsma ◽  
Frédéric Baudron ◽  
Moti Jaleta ◽  
Kindie Tesfaye ◽  
...  
Keyword(s):  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Wheat Yield ◽  
Farming Systems ◽  
Smallholder Farming ◽  
Yield Gap ◽  
Frontier Analysis ◽  
Ecological Zones ◽  
Relative Contribution ◽  
Yield Gaps

AbstractWheat yields in Ethiopia need to increase considerably to reduce import dependency and keep up with the expected increase in population and dietary changes. Despite the yield progress observed in recent years, wheat yield gaps remain large. Here, we decompose wheat yield gaps in Ethiopia into efficiency, resource, and technology yield gaps and relate those yield gaps to broader farm(ing) systems aspects. To do so, stochastic frontier analysis was applied to a nationally representative panel dataset covering the Meher seasons of 2009 and 2013 and crop modelling was used to simulate the water-limited yield (Yw) in the same years. Farming systems analysis was conducted to describe crop area shares and the availability of land, labour, and capital in contrasting administrative zones. Wheat yield in farmers’ fields averaged 1.9 t ha− 1 corresponding to ca. 20% of Yw. Most of the yield gap was attributed to the technology yield gap (> 50% of Yw) but narrowing efficiency (ca. 10% of Yw) and resource yield gaps (ca. 15% of Yw) with current technologies can nearly double actual yields and contribute to achieve wheat self-sufficiency in Ethiopia. There were small differences in the relative contribution of the intermediate yield gaps to the overall yield gap across agro-ecological zones, administrative zones, and farming systems. At farm level, oxen ownership was positively associated with the wheat cultivated area in zones with relatively large cultivated areas per household (West Arsi and North Showa) while no relationship was found between oxen ownership and the amount of inputs used per hectare of wheat in the zones studied. This is the first thorough yield gap decomposition for wheat in Ethiopia and our results suggest government policies aiming to increase wheat production should prioritise accessibility and affordability of inputs and dissemination of technologies that allow for precise use of these inputs.

Sign in / Sign up

Export Citation Format

Share Document