SITE INDEX FOR Pinus greggii Engelm. PLANTATIONS IN METZTITLÁN, HIDALGO, MEXICO

The Diagnosis and Recommendation Integrated System (DRIS) has been used successfully in agricultural crops and holds promise for use in forest stands. This study used soil tests to develop DRIS norms and evaluate their effectiveness in coastal Douglas-fir (Pseudotsugamenziesii (Mirb.) Franco) forests. DRIS norms for nitrogen, phosphorus, potassium, and calcium were developed using soil test and site index data from 72 soil series that commonly support Douglas-fir in western Washington. The norms were tested using soil test and stand basal area growth response data from 20 thinned and 30 unthinned N fertilizer test sites in coastal Washington and Oregon. Response to urea fertilizer in thinned stands averaged 34% and 43% for 224 and 448 kg N•ha−1, respectively, when N was identified as the most limiting nutrient. When N was not the most limiting nutrient, N response averaged 8% and 10% for 224 and 448 kg N•ha−1, respectively. Results were similar in unthinned stands and thinned stands, although response to fertilizer appeared to be slightly less in unthinned stands when N was the most limiting nutrient. DRIS correctly classified 25 of the 33 sites (76%) where N fertilizer increased growth by more than 15%. More importantly, 13 of the 17 (76%) sites that responded by less than 15% were correctly identified by DRIS. The results clearly indicate that N fertilizer response is dependent on the interactions (balance) between soil nutrients at a given site. Future soil diagnostic work needs to focus on techniques, like DRIS, that provide an assessment of these interactions.

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Supply Potential and Annual Availability of Timber and Forest Biomass Resources for Energy Considering Inter-Prefectural Trade in Tohoku Region of Japan

Environmental Sciences Proceedings ◽

10.3390/iecf2020-08025 ◽

2020 ◽

Vol 3 (1) ◽

pp. 40

Author(s):

Yusuke Matsuoka ◽

Hiroaki Shirasawa ◽

Uichi Hayashi ◽

Kazuhiro Aruga

Keyword(s):

Tree Species ◽

Forest Biomass ◽

Wood Chip ◽

Site Index ◽

Geographical Survey Institute ◽

Digital Elevation ◽

Gis Data ◽

Biomass Resource ◽

Biomass Resources ◽

Generation Capacity

To promote sustainable timber and forest biomass utilization, this study estimated technically feasible and economically viable availability considering forest regenerations. This study focuses on five prefectures, namely, Aomori, Iwate, Miyagi, Akita, and Yamagata, and considers the trade between these prefectures. The data used in this study include forest registration (tree species and site index) and GIS data (information on roads and subcompartment layers) from the prefectures for private and communal forests. Additionally, this study includes GIS data (subcompartment layers, including tree species) from the Forestry Agency of Japan for national forests as well as 10-m-grid digital elevation models (DEMs) from the Geographical Survey Institute. As a result, supply potentials of timber and forest biomass resources were estimated at 11,388,960 m3/year and 2,277,792 m3/year, respectively. Then, those availabilities were estimated at 1,631,624 m3/year and 326,325 m3/year. Therefore, the rate of availabilities to supply potentials was 14.3%. Since timber production, and wood chip usage from thinned woods and logging residues in 2018 were 4,667,000 m3/year and 889,600 m3/year, respectively, the rates of timber and forest biomass resource availabilities to those values were 35.0% and 36.7%, respectively. Furthermore, the demand was estimated at 951,740 m3/year from 100,000 m3/year with the generation capacity of 5 MW. The rate of forest biomass resource availability versus the demand was 34.2%. The rates were increased to 64.1% with an additional regeneration subsidy, 173.3% with the thinning subsidy, and 181.5% with both subsidies. Thus, the estimated availability with both subsidies met the demand sufficiently in this region.

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Mapping forest age using National Forest Inventory, airborne laser scanning, and Sentinel-2 data

Forest Ecosystems ◽

10.1186/s40663-020-00274-9 ◽

2020 ◽

Vol 7 (1) ◽

Author(s):

Johannes Schumacher ◽

Marius Hauglin ◽

Rasmus Astrup ◽

Johannes Breidenbach

Keyword(s):

Forest Inventory ◽

Regression Models ◽

Laser Scanning ◽

Site Index ◽

National Forest Inventory ◽

Airborne Laser Scanning ◽

National Forest ◽

Practical Applications ◽

Airborne Laser ◽

Sentinel 2

Abstract Background The age of forest stands is critical information for forest management and conservation, for example for growth modelling, timing of management activities and harvesting, or decisions about protection areas. However, area-wide information about forest stand age often does not exist. In this study, we developed regression models for large-scale area-wide prediction of age in Norwegian forests. For model development we used more than 4800 plots of the Norwegian National Forest Inventory (NFI) distributed over Norway between latitudes 58° and 65° N in an 18.2 Mha study area. Predictor variables were based on airborne laser scanning (ALS), Sentinel-2, and existing public map data. We performed model validation on an independent data set consisting of 63 spruce stands with known age. Results The best modelling strategy was to fit independent linear regression models to each observed site index (SI) level and using a SI prediction map in the application of the models. The most important predictor variable was an upper percentile of the ALS heights, and root mean squared errors (RMSEs) ranged between 3 and 31 years (6% to 26%) for SI-specific models, and 21 years (25%) on average. Mean deviance (MD) ranged between − 1 and 3 years. The models improved with increasing SI and the RMSEs were largest for low SI stands older than 100 years. Using a mapped SI, which is required for practical applications, RMSE and MD on plot level ranged from 19 to 56 years (29% to 53%), and 5 to 37 years (5% to 31%), respectively. For the validation stands, the RMSE and MD were 12 (22%) and 2 years (3%), respectively. Conclusions Tree height estimated from airborne laser scanning and predicted site index were the most important variables in the models describing age. Overall, we obtained good results, especially for stands with high SI. The models could be considered for practical applications, although we see considerable potential for improvements if better SI maps were available.

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A Growth and Yield Model for Improved Eastern Cottonwood Plantations in the Lower Mississippi Delta

Southern Journal of Applied Forestry ◽

10.1093/sjaf/15.4.213 ◽

1991 ◽

Vol 15 (4) ◽

pp. 213-216 ◽

Cited By ~ 7

Author(s):

Quang V. Cao ◽

Kenneth M. Durand

Keyword(s):

Populus Deltoides ◽

Mississippi Delta ◽

Basal Area ◽

Site Index ◽

Growth And Yield ◽

Annual Increment ◽

Yield Model ◽

Eastern Cottonwood ◽

Volume Yield ◽

Growth And Yield Model

Abstract A compatible growth and yield model was developed based on remeasurement data collected from 183 plots on unthinned improved eastern cottonwood (Populus deltoides Bartr.) plantations in the lower Mississippi Delta. The Sullivan and Clutter (1972) equation form was selected for predicting cubic-foot volume yield and projecting volume from site index and initial age and basal area. Yield equations explained 97% and 94%, respectively, of the variations in total outside bark and merchantable inside bark volumes. Mean annual increment of merchantable volume culminated between 8 and 15 years, depending on site index and initial basal area. South. J. Appl. For. 15(4):213-216.

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Validation of forest height–age models

Canadian Journal of Forest Research ◽

10.1139/x26-090 ◽

1996 ◽

Vol 26 (5) ◽

pp. 810-818 ◽

Cited By ~ 6

Author(s):

Gordon D. Nigh ◽

Vera Sit

Keyword(s):

Forest Management ◽

Confidence Intervals ◽

Error Variance ◽

Site Index ◽

Random Coefficients ◽

Multivariate Technique ◽

Forest Height ◽

Preliminary Validation ◽

Age Model ◽

Validation Procedure

Forest height–age models are used in forest management to estimate height and (or) site index. It is useful to know the bias and precision of these models in order to evaluate their applicability. Methods are available for validating the models; however, many problems exist with the methods because of a lack of independence in the data and nonconstant error variance across a range of ages. A validation procedure is presented that overcomes these problems by using a multivariate technique (random coefficients) to model the structure of the errors associated with the models. Confidence intervals for bias and precision can then be constructed based on the error structure. This method of validation was demonstrated on the white spruce (Piceaglauca (Moench) Voss) height–age model for British Columbia, Canada. The preliminary validation showed the model to be unbiased for estimating both height and site index; however, its precision was poor.

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Selecting a functional form for anamorphic site index curve estimation

Forest Ecology and Management ◽

10.1016/s0378-1127(98)00501-5 ◽

1999 ◽

Vol 118 (1-3) ◽

pp. 211-221 ◽

Cited By ~ 8

Author(s):

David M Nanang ◽

Thompson K Nunifu

Keyword(s):

Functional Form ◽

Site Index ◽

Curve Estimation ◽

Site Index Curve ◽

Index Curve

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Linking climate, gross primary productivity, and site index across forests of the western United States

Canadian Journal of Forest Research ◽

10.1139/x11-086 ◽

2011 ◽

Vol 41 (8) ◽

pp. 1710-1721 ◽

Cited By ~ 53

Author(s):

Aaron R. Weiskittel ◽

Nicholas L. Crookston ◽

Philip J. Radtke

Keyword(s):

Climate Change ◽

Growth Models ◽

Gross Primary Production ◽

Site Index ◽

Nonparametric Model ◽

Climate Change Scenarios ◽

Geographic Scale ◽

Potential Productivity ◽

The One ◽

The Relationship

Assessing forest productivity is important for developing effective management regimes and predicting future growth. Despite some important limitations, the most common means for quantifying forest stand-level potential productivity is site index (SI). Another measure of productivity is gross primary production (GPP). In this paper, SI is compared with GPP estimates obtained from 3-PG and NASA’s MODIS satellite. Models were constructed that predict SI and both measures of GPP from climate variables. Results indicated that a nonparametric model with two climate-related predictor variables explained over 68% and 76% of the variation in SI and GPP, respectively. The relationship between GPP and SI was limited (R2 of 36%–56%), while the relationship between GPP and climate (R2 of 76%–91%) was stronger than the one between SI and climate (R2 of 68%–78%). The developed SI model was used to predict SI under varying expected climate change scenarios. The predominant trend was an increase of 0–5 m in SI, with some sites experiencing reductions of up to 10 m. The developed model can predict SI across a broad geographic scale and into the future, which statistical growth models can use to represent the expected effects of climate change more effectively.

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