Performance of softwood plantation sawmills: the volume vs. value sawing strategy

Performance of softwood plantation sawmills: the volume vs. value sawing strategy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performance of softwood plantation sawmills: the volume vs. value sawing strategy John Ngobi, Robert Kyeyune Kambugu, Paul Mugabi, Abwoli Yabezi Banana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4943760/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sawmill performance is anchored on three indicators: timber volume recovery, timber value recovery and log throughput. Traditionally, sawyers use the volume sawing strategy aimed at maximizing timber volume recovery. The question is, does the volume sawing strategy result into better performance or an alternative strategy or a hybrid of strategies would yield better results. To answer this question, this study determined timber volume and timber value recovery under the volume and value sawing strategy. Data were collected from logs randomly selected from four sawmills and grouped using cluster analysis. PHP programming language was used to determine sawing patterns that maximized timber volume and/or value from each log. The difference in timber volume and value recovery between the volume and value sawing strategy was tested using a paired t-test at 5% significance level. The value sawing strategy yielded significantly (p < 0.05) higher timber volume than the value sawing strategy except for in smaller logs (10-20cm). Timber value recovery was significantly higher (p < 0.05) under the value sawing strategy than volume sawing strategy for all log sizes. Mean reduction in timber volume recovery was 2% whereas the increment in timber value recovery was 12% under the value sawing strategy. Adoption of the value sawing strategy by the sawmills was recommended since it indicated a potential for improved sawmill profitability. Forestry Sawing strategy Volume sawing strategy Value sawing strategy Timber volume recovery Timber value recovery log throughput Figures Figure 1 INTRODUCTION Sawmills aim at maximizing profits made from the production and sale of sawn timber and other by-products including slabs, saw dusts or wood chips (Lundahl and Gronlund 2010 ). To achieve this objective, sawmills need to be efficient i.e., effectively utilize manpower, machine and raw material (Lundahl 2009 ). According to Quebec et al. ( 2015 ), three key performance indicators must be optimized for sawmills to remain competitive and profitable: the volume of logs milled per productive hour relative to the sawmill capacity i.e. log throughput; the volume of sawn timber produced per unit volume of logs milled i.e. volume recovery; and the monetary value of sawn timber produced per unit volume of logs milled i.e. value recovery. Maximizing log throughput requires a sawyer to properly plan and eliminate bottlenecks in the milling process, reduce machine breakdown through preventative maintenance and effectively manage labor Nwanya et al. ( 2017 ). This eventually results into effective utilization of both machinery and human resources and increases profit margins by reducing the milling costs (Missanjo and Magodi 2015 ). Maximizing timber volume recovery is concerned with optimizing sawmill profits by recovering more volumes of sawn timber. This is important since sawn timber is the most valuable and saleable sawmill product (Taube et al. 2020 ). Additionally, increasing timber volume recovery reduces the unit cost of sawlogs and thus the overall milling costs (Rawat et al. 2023 ). For instance, the unit sawlog cost for a sawmill incurring a milling cost of 30 USD/m 3 at 30% can be halved to 50 USD/m 3 when timber volume recovery is doubled to 60%. On the other hand, timber value recovery is concerned with maximizing profits by sawing the more valuable sawn timber grades (Walker 2006 ). According to Mendes and Pasiecznik ( 2015 ), this is important since sawn timber volume might not reflect the true monetary value of such timber on the market. For example, a roughly sawn timber piece of 8x1x14 will likely fetch a premium price than a 4x2x14 piece even though they have equal volumes. Therefore, sawyers ought to put into consideration the monetary value attached to each timber size while selecting the sawing pattern to use on a particular log. Incidentally, it is practically impossible to simultaneously maximize all the performance indicators in the real sawmilling environment (Steele et al. 1993 ; Todoroki and Ronnqvist 1999 ; Lindner and Wessels 2015 ; Vergara et al. 2015 ). This is because the sawing pattern that maximizes throughput may not maximize the resultant volume or value of sawn timber produced. A study by Steele et al. ( 1993 ) indicated that maximizing timber volume recovery would reduce the timber value by 2 USD per log sawn by the sawmill whereas maximizing timber value would reduce timber volume recovery by 2%. Nordmark ( 2005 ) reported a 3% drop in timber volume recovery if a timber value sawing strategy was adopted. While the volume sawing strategy is the most common sawing strategy used by sawmills (Missanjo and Magodi 2015 ; Ngobi 2019 ), it is not evident that it is better than the value sawing strategy in ensuring the overall competitiveness and profitability of sawmills (Quebec et al. 2015 ; Taube et al. 2020 ). This study determined the performance of sawmills under the timber volume and timber value sawing strategies. This was required as a basis for determining which of the two sawing strategies can better optimize the net revenues from sale of sawn timber by the sawmills. METHODOLOGY Study area The study was conducted in the Eastern, Central and Albertine plantation clusters in Mayuge, Mubende, Hoima and Masindi districts respectively (Fig. 1 ). Sampling Plantation clusters were selected purposively based on accessibility and potential number of sawmills. Sawmills were also purposively selected to include different technologies. Logs were selected systematically to obtain a sample (N i ) of at least 90 logs in three days for each sawmill (Table 1 ). Table 1 sampling intensities used Sample sawmill N d N q k N l Medium band sawmill 3 500 14 106 Mobile band sawmill-A 4 60 2 98 Mobile band sawmill-B 4 60 2 100 Mobile circular sawmill 4 65 2 103 Sampling interval (k) was determined using Equation i. The starting sample log was the first log on the sawmill log deck for the day. $$\:k=\frac{{N}_{q}*{N}_{d}}{{N}_{l}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[i\right]$$ Where: N q = Average number of logs sawn at sawmill per day as obtained from the sawyers. N d = Target number of days to be spent at a sawmill which was 3 given resource constraints, N l = Number of logs to be sampled (≥ 90). Data collection For each sampled log, bark thickness and log length were measured using a measuring tape. The small and butt end diameters were measured using a caliper. Thickness of sawblades used at each sample sawmill was measured using a caliper. The unit price of each timber size produced and the sawing method were also recorded. Data analysis For each log, timber volume recovery and value recovery of each applicable sawing pattern were calculated using equations ii and iii respectively. $$\:{T}_{y}=\:\frac{{P}_{y}}{{V}_{l}}\times\:100\%\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[ii\right]\:\:\:\:\:$$ $$\:{R}_{y}=\:\frac{\sum\:_{i}{P}_{i}{N}_{iy}}{{V}_{l}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[iii\right]\:\:\:\:\:$$ Where: T y = timber volume recovery obtained from a log using sawing pattern y, R y = timber value recovery obtained from a log sawn using sawing pattern y, V l = log volume (m 3 ) obtained from log volume table of Pinus caribaea based on small end diameter and length, P y = timber volume (m 3 ) produced from a log using sawing pattern y, P i = price per timber piece (UGX) of size i, N i = number of pieces of timber of size i produced using sawing pattern y. Timber volume produced from a log using a given sawing pattern was calculated from Equation iv. $$\:{P}_{y}=\:\frac{\sum\:_{i}{n}_{i}{w}_{i}{h}_{i}{l}_{i}}{Q}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[iv\right]$$ Where: n i = number of pieces of timber of size i sawn, w i = nominal width (mm) of timber of size i sawn, h i = nominal thickness (mm) of timber of size i sawn, l i = length (mm) of timber of size i sawn, Q = conversion factor (1x10 6 ) from mm 3 to m 3 . A sawing pattern was a combination of timber size as center piece/s and any corresponding extractable timber as either side, top, or bottom pieces or a combination of them. For each log, possible sawing patterns were developed following the sawing method (cant sawing) used by the study sawmill. The study adapted and/or modified mathematical algorithms from Maness and Adams ( 1991 ) and Ngobi ( 2019 ) and developed sawing patterns in three steps indicated below. Step 1: Determining the maximum number of center pieces. For each sample log, the length (C l ), width (C w ) and height (C h ) of possible center pieces that could be extracted were subjected to constraints in equations v, vi and vii respectively. $$\:{C}_{l}\:\le\:l\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[v\right]$$ $$\:{C}_{w}<sed\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[vi\right]$$ $$\:{C}_{h}\le\:2{\{{r}^{2}-{\left(0.5\times\:{C}_{w}\right)}^{2}\}}^{0.5}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[vii\right]$$ Where: l = log length, sed = log small end diameter under the bark, r = radius of log small end diameter under the bark. The length of the cant/center piece had to be at most equal to the log length (Equation v) whereas the width must be less than the small end diameter of the log under the bark (Equation vi). Equation vii was formulated by Maness and Adams ( 1991 ) using Pythagorean Theorem and required that the corresponding thickness of the cant be at most equal to the highest rectangle of width (C w ) that can fit into a circle repsenting the small end dimeter under the bark (sed). Small end diameter under the bark was obtained from Equation viii as in Sedmíková et al. ( 2020 ). $$\:sed=\:\:\:\frac{{sed}_{1}\:\:-2*b\:}{c}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[viii\right]$$ Where: sed 1 = log small end diameter (cm) measured over the bark, c = conversion factor from cm to mm. Radius of log small end diameter under the bark was obtained by halving sed. For each timber size that could be extracted as center piece, the maximum number of pieces were obtained from Equation ix as in Maness and Adams ( 1991 ). $$\:n=\:\:\:\frac{2{\{{r}^{2}-{\left(0.5\times\:cw\right)}^{2}\}}^{0.5}\:+s}{ch+s}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[ix\right]$$ Where: s = kerf width (mm), n = maximum number of center pieces of timber with width (cw) and thickness (ch). n was rounded off, down to the nearest whole number when found to a floating figure since the number of timber pieces must be an integer. Kerf width was obtained from Equation x as in Ngobi ( 2019 ) $$\:s=b+2\times\:s\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[x\right]$$ Where: b = blade thickness (mm), s = blade setting (mm) Step 2: Determining possible side pieces to be extracted. The study assumed minimal log eccentricity so that the two resulting side slabs after extracting the center piece/s were of equal size. The timber size that could be extracted from the side slabs as side piece/s was constrained by its thickness (S h ) and width (S w ), width of the center piece (c w ) and radius of small log end diameter (r) as in equation xi. $$\:{{\left\{\right(0.5\:\times\:cw\:+p*s\:+{S}_{{h}_{r-1}\:\:}+{S}_{{h}_{r}\:\:}\:)}^{2}\:+{\left(0.5\times\:\:{S}_{{w}_{r}}\right)}^{2}\}}^{0.5\:}<r\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xi\right]$$ p represented the position of side piece i.e., 1 for inner most side piece extracted next to center piece, 2 for second sidepiece extracted after the inner most piece, 3 for third sidepiece et-cetera. The physical meaning of Equation xi was explained in Ngobi ( 2019 ). Step 3: Determining possible top and bottom pieces in each sawing pattern. Timber pieces which were narrower and/or thinner than centers pieces and could be extracted from the top and bottom slabs as top and bottom pieces were subjected to similar constraints as side pieces. However, the planes of center pieces were reversed as explained in Ngobi ( 2019 ) and indicated in equation xii. $$\:{{\left\{\right(0.5*(n+\left(n-1\right)*s)+r*s\:+\:{tb}_{{w}_{r-1}\:\:}+{tb}_{{w}_{r}\:\:})}^{2}\:+{(0.5\times\:{tb}_{{h}_{r}})}^{2}\}}^{0.5\:}<R\:\:\:\:\:\:\:\:\left[xii\right]$$ Where: tb w = width of top/bottom piece, tb h = thickness of top/bottom piece, r-1 = preceding top/bottom piece if any. PHP programming language was used to code the mathematical algorithm and generate possible sawing patterns for each sampled log. For each log, the sawing pattern that yielded the highest timber volume recovery (T max ) from equation ii above was identified. This was the volume pattern and the resulting volume recovery was the recovery considered under the volume sawing strategy. The corresponding value recovery (R a ) of volume pattern was obtained using equation xiii. $$\:{R}_{a}=\:\frac{\sum\:_{i}{P}_{i}{N}_{ia}}{{V}_{l}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xiii\right]\:\:\:\:\:$$ Where: N ia = number of timber of size i produced using the volume pattern. On the other hand, the sawing pattern that yielded the highest timber value recovery (R max ) as obtained in equation iii above was the value pattern and was considered under the value sawing strategy. The corresponding volume recovery (T a ) of the value pattern was obtained using equation xiv. $$\:{T}_{a}=\:\frac{{P}_{a}}{{V}_{l}}\times\:100\%\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xiv\right]$$ Where: P a = timber volume yielded by the value pattern. Cluster analysis was used to group logs into classes based on small end diameter. For each log class, timber volume recovery (T r ) and value recovery (R r ) under the volume sawing strategy were calculated using equations xv and xvi respectively. $$\:{T}_{r}=\:\:\:\:\:\:\:\:\:\frac{\sum\:{T}_{max}}{{N}_{r}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xv\right]$$ $$\:{R}_{r}\:=\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\frac{\sum\:{R}_{av}}{{N}_{r}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xvi\right]$$ Where: N r = number of logs in the log class r, R av = value recovery of volume pattern for log v in class r. Timber value recovery and volume recovery under the value sawing strategy were also calculated using a similar approach as in equations xv and xvi respectively. The weighted timber volume recovery (T s ) and value recovery (R s ) of each sawmill under the volume sawing strategy were calculated using equations xvii and xviii respectively. $$\:{T}_{s}=\:\:\sum\:_{r}{T}_{r}*\frac{{V}_{r}}{V}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xvii\right]$$ $$\:{R}_{s}=\:\:\sum\:_{r}{R}_{r}*\frac{{V}_{r}}{V}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left[xviii\right]$$ Where: V r = Total volume of logs (m 3 ) in log class r, V = Total volume of logs (m 3 ) sampled at the sawmill. The weighted timber volume and value recovery of each sawmill under the value sawing strategy were calculated using a similar approach as in equations and xvii and xviii respectively. The difference in timber volume recovery between the volume and value sawing strategy was tested using a paired t-test at 5% significance level. A paired t-test was also used to test the difference in timber value recovery between the volume and value sawing strategy. RESULTS Diameter characterization of logs Log small end diameter ranged from 12 cm to 32cm with a mean of 20cm. Medium band sawmill sawed logs with larger diameters relative to other sawmills. The mobile circular sawmill sawed logs with the smallest diameter (Table 2 ). Table 2 Characteristics of logs sawn by sampled sawmills Sawmill category Log diameter class Weighted Average 10-19cm 20-25cm ≥ 26cm 1 18 22 28 25 2 19 21 26 19 3 20 21 25 19 4 19 22 28 18 Average 16 22 27 20 1 = Medium band sawmill, 2 = Mobile band sawmill-A, 3 = Mobile band sawmill-B, 4 = Mobile circular sawmill. Timber volume recovery Volume sawing strategy yielded higher timber volume recovery than the value sawing strategy except for smaller logs (10cm-19cm). The medium band sawmill had the highest volume recovery under both strategies whereas the mobile circular sawmill had the lowest volume recovery. Timber volume recovery increased by 3%, 2% and 1% with the volume sawing strategy for the medium band sawmill, mobile band sawmill and mobile circular sawmill respectively (Table 3 ). Table 3 Timber volume recovery for volume strategy (V) and value strategy (R) Sawmill type 10-19cm 20-25cm ≥ 26cm Weighted Average V R V R V R V R 1 34 34 41 40 50 49 49 46 2 36 36 43 42 48 46 39 37 3 36 36 43 42 48 46 38 36 4 33 33 41 40 43 42 31 30 Average 34 34 42 41 48 46 39 37 1 = Medium band sawmill, 2 = Mobile band sawmill-A, 3 = Mobile band sawmill-B, 4 = Mobile circular sawmill. A paired t-test indicated a significant difference (p < 0.05) in timber volume recovery between sawing strategies. Volume recovery was significantly different for all log classes at all sawmills except for smaller logs with 10cm-19cm. (Table 4 ). Table 4 Paired samples test on volume recovery between volume and value strategy Sawmill category Log class Mean Std. Deviation Std. Error 95% C. I t df Sig (2-tailed) Lower Upper 1 10–19 0.00 2.33 0.7 -0.2 0.3 1.94 10 0.70 20–25 0.00 0.9 0.52 0.39 0.23 3.71 86 0.00 ≥ 26 0.00 0.95 0.75 0.19 0.48 4.49 158 0.00 2 10–19 0.00 1.0 0.21 0.7 0.32 2.21 18 0.20 20–25 0.62 2.01 0.3 0.09 1.29 2.41 64 0.02 ≥ 26 1.22 1.53 0.32 0.1 2.43 2.34 13 0.04 3 10–19 0.00 1.34 0.31 0.67 0.34 2.33 18 0.30 20–25 0.69 2.41 0.29 0.93 1.29 2.31 64 0.02 ≥ 26 1.21 1.92 0.52 0.10 2.33 2.35 13 0.04 4 10–19 0.00 1.74 0.24 1.23 0.23 0.682.99 48 0.10 20–25 1.42 2.37 0.36 0.68 2.14 3.92 42 0.00 ≥ 26 1.00 0.88 0.16 0.68 1.31 6.43 31 0.00 1 = Medium band sawmill, 2 = Mobile band sawmill-A, 3 = Mobile band sawmill-B, 4 = Mobile circular sawmill. Timber value recovery Value sawing strategy was associated with higher timber value recovery than the volume sawing strategy for all log classes at all sawmills. Larger logs had the highest value recovery under value sawing strategy (278,000shs/m 3 ) whereas smaller logs had the lowest recovery of 161,000shs/m 3 . The respective value recovery rates under the volume sawing strategy were 229,000shs/m 3 and 155,000shs/m 3 . The medium band sawmill had the highest value recovery for all log classes under both strategies whereas the mobile circular sawmill had the lowest value recovery (Table 5 ). Table 5 Value recovery (1000shs/m 3 ) for volume strategy (V) and value strategy (R) Sawmill category 10-19cm 20-25cm ≥ 26cm Weighted Average V R V R V R V R 1 249 251 293 302 356 432 329 377 2 137 138 159 165 209 267 136 151 3 134 136 158 164 208 267 132 143 4 109 119 134 144 143 146 99 109 Average 155 161 183 195 229 278 174 195 1UGX = 0.00026USD, 1 = Medium band sawmill, 2 = Mobile band sawmill-A, 3 = Mobile band sawmill-B, 4 = Mobile circular sawmill. There was a significant difference (p < 0.05) in timber value recovery between the sawing strategies for all log sizes at all sawmills except smaller logs with diameter below 20cmm (Table 6 ). Table 6 Paired samples test on timber value recovery between sawing strategies Sawmill category Log class Mean Std. Deviation Std. Error 95% C. I t df Sig (2-tailed) Lower Upper 1 10–19 -10,400 16,300 4,900 -21,400 600 -2.11 10 0.6 20–25 -8,900 21,500 2,300 -13,500 -4,300 -3.86 86 0.00 ≥ 26 -73,900 50,500 4,000 -81,800 -66,000 -18.47 158 0.00 2 10–19 -1,800 5,400 1,200 -4,400 800 -1.45 18 0.16 20–25 -13,000 12,800 1,500 -16,000 -9,900 -8.23 64 0.00 ≥ 26 -58,300 53,800 14,400 -89,400 -27,000 -4.05 13 0.00 3 10–19 -1,800 5,400 1,200 -4,400 800 -1.46 18 0.16 20–25 -13,000 12,800 1,500 -16,200 -9,900 -8.24 64 0.00 ≥ 26 -58,300 53,800 14,400 -89,000 -27,200 -4.00 13 0.00 4 10–19 -8,900 9,000 1,200 -11,500 -6,300 -6.900 48 0.06 20–25 -13,800 8,200 1,300 -16,500 -11,200 -10.00 42 0.00 ≥ 26 3,200 3,900 700 -4,600 -1,800 -4.64 31 0.00 1 = Medium band sawmill, 2 = Mobile band sawmill-A, 3 = Mobile band sawmill-B, 4 = Mobile circular sawmill DISCUSSION The volume sawing strategy yielded higher timber volume recovery than the value sawing strategy in medium and larger logs implying that the sawing patterns that maximized timber volume recovery did not yield maximum timber value recovery for such logs. Similar results were also obtained in Steele et al. ( 1993 ), Todoroki and Ronnqvist ( 1999 ) and Nordmark ( 2005 ). The lack of difference in timber volume recovery between the sawing strategies for the smaller logs can be attributed to limited number of possible timber sizes that can be extracted from such logs and thus fewer applicable sawing patterns. Under the volume sawing strategy, timber volume recovery increased with log sizes. Increase in timber volume recovery with log diameter has also been reported in Kambugu et al. ( 2005 ), Missanjo and Magodi ( 2015 ), and Ngobi ( 2019 ) and holds if the larger trees, which are generally older are harvested while still with large volumes of sound wood (Steele, 1984 ). The medium band sawmill had the highest timber volume recovery for each log size and this can be attributed to the relatively larger diameter logs that were sawn by the sawmill. Additionally, the medium band sawmill was equipped with optimizing edgers and resaws and recovered narrower, thinner and/or shorter sawn timber pieces. The mobile circular sawmill had the lowest timber volume recovery due to the relatively small diameter log sizes that it sawed. Moreover, the mobile circular sawmill had the thickest saw blade which resulted into a wide saw-kerf and consequently, conversion of larger volumes of logs into saw-dust. According to Kambugu et al. ( 2005 ), sawmills with thick saw blades are inefficient and inappropriate for use in conversion of small diameter logs. Assuming an harvesting and milling cost of 150,000UGX/m 3 (FAO, 2020 ), zero cost for saw logs since the sawmills owned the forest plantations, and timber value recovery obtained in Table 5 under the volume sawing strategy, the net revenue from sale of sawn timber for each respective log size were 99,000UGX. 143,000UGX and 206,000UGX for the medium band sawmill and − 41,000 UGX, -16,000 UGX and − 7,000 UGX for the mobile circular sawmill. Therefore, the mobile circular sawmill did not realize profits from sale of sawn timber for all the log diameter sizes. On the other hand, the medium band sawmill was profitable even with no consideration of revenue obtained from sale of by-products. Mobile band sawmills which had relatively higher timber volume recovery than the mobile circular sawmill only realized a positive net revenue from medium and larger log diameter sizes i.e., -14,500 UGX, 8,500 UGX and 58,500 UGX for smaller, medium and larger log sizes respectively. Timber value recovery obtained under the value sawing strategy was higher than that of the volume sawing strategy except for the small log sizes.Therefore, log sawing patterns that maximized timber value recovery did not maximize timber volume recovery in medium and large logs. Consequently, the value sawing strategy maximized timber value recovery at the expense of timber volume recovery except in these two log sizes. The medium band sawmill had the highest timber value recovery under the value sawing strategy which can be attributed to the relatively higher volume of timber recovered from the logs and the higher prices attached to the sawn timber produced. Compared to the volume sawing strategy, timber value recovery increased by 1%, 3% and 21% for the respective log sizes when the value sawing strategy was adopted at the medium band sawmill. This is generally higher than the 3% increase in timber value recovery reported in Todoroki and Ronnqvist ( 1999 ) when the value sawing strategy was considered. The net revenue from the sale of sawn timber for the respective log sizes increased by 2%, 6% and 37% at the medium band sawmill. Although the net revenue from the sale of sawn timber increased for the mobile circular sawmill by 24%, 62% and 43% for the respective log sizes, the sawmill still had a negative net revenue from sale of sawn timber under the value sawing strategy. The mobile band sawmills had their net revenue increased by 10%, 70% and 32% for the respective log sizes. The lower increase in net revenues obtained from larger logs when the value strategy was adopted at the mobile circular and band sawmill can be attributed to the fact that timber pricing is not only dictated by the corresponding timber volume but also the prevailing timber demand, log requirement, present stock and production costs (Kant, 2010 ). On the other hand, the practice of attaching premium prices on wider timber pieces milled from only larger logs might explain the relatively higher increase in net revenue associated with the larger log sizes at the medium band sawmill. CONCLUSION The volume sawing strategy increased timber volume recovery in the medium (1%) and larger logs (2%) but did not affect timber volume recovery in smaller logs at all sawmills. The highest and lowest increment were obtained at the medium band sawmill and mobile circular sawmill respectively. The corresponding timber value recovery declined by 3%, 6% and 17% in smaller, medium and larger logs. Timber volume recovery dropped with the value sawing strategy but the timber value recovery was maximized at 161, 000UGX 195, 000UGX and 278, 000UGX for smaller, medium and larger logs. The net revenue from sale of sawn timber from the respective log sizes increased by 120%, 32% and 62% with adoption of the value sawing strategy. There is a need to study the effect of the two sawing strategies on log throughput for the different sawmills. The value sawing strategy should be adopted for all log sizes at all sawmills since it indicated potential for improving the overall profitability of the sawmills. Declarations ACKNOWLEDGMENT This study was funded by the Carnegie Corporation through Makerere University, Directorate of Research and Graduate Training under the Supporting Early-Career Academics (SECA-2019) Project. References FAO (2020) Unlocking future investments in Uganda’s commercial forest sector Johansson M (2007) Product Costing for Sawmill Business Management. Vaxjo University Kambugu R, Banana AY, Zziwa A, Agea G, Kabogoza J (2005) Relative efficiency of sawmill types operating in Uganda ’ s softwood plantations. Uganda J Agricultural Sci 11:14–19 Kant S (2010) Market, timber pricing, and forest management. For Chron 86(5):580–588. https://doi.org/10.5558/tfc86580-5 Lindner B, Wessels CB (2015) Determining optimal primary sawing and ripping machine settings in the wood manufacturing chain Determining optimal primary sawing and ripping machine settings in the wood manufacturing chain. For Sci 12. https://doi.org/10.2989/20702620.2014.1001678 Lundahl CG (2009) Total Quality Management in Sawmills. Lulea University of Technology Lundahl CG, Gronlund A (2010) Increased yield in sawmills by applying alternate rotation and lateral positioning. For Prod Soc 60(4):331–338 Maness t, Adams D (1991) The combined optimization of log bucking and sawing strategies. Wood Fiber Sci 23(2):296–314 Mendes A, Pasiecznik N (2015) In: Davis C, Jarvis W, Kerrett R, Marshall G, Welcome L (eds) From cutting to order to cutting for value A handbook for chainsaw millers. A handbook for chainsaw millers. Tropenbos International Missanjo E, Magodi F (2015) Impact of taper and sawing methods on lumber volume recovery for Pinus kesiya and Pinus patula logs in circular sawmills. J For Prod Industries 4(1):12–16 Ngobi J (2019) Improvement of timber recovery from pine sawlogs using a band sawmill. Afr J Rural Dev 4(September):421–429 Nordmark U (2005) Value recovery and production control in the forestry-wood chain using simulation technique . 224. http://epubl.ltu.se/1402-1544/2005/21/LTU-DT-0521-SE.pdf Nwanya SC, Udofia JI, Ajayi OO (2017) Optimization of machine downtime in the plastic manufacturing. Cogent Eng 4(1):1–12. https://doi.org/10.1080/23311916.2017.1335444 Quebec M, Hassegawa M, Havreljuk F, Ouimet R, Auty D, Pothier D (2015) Large-Scale Variations in Lumber Value Recovery of Yellow Birch and Sugar Maple in Quebec, Canada. PLoS ONE. August. https://doi.org/10.1371/journal.pone.0136674 Rawat YS, Eba M, Nebiyu M (2023) Lumber Recovery Rate of Cupressus lusitanica in Arsi Forest Enterprise, Ethiopia. Sustain (Switzerland) 15(2). https://doi.org/10.3390/su15021046 Sedmíková M, Löwe R, Jankovský M, Natov P, Linda R, Dvořák J (2020) Estimation of over-and under-bark volume of scots pine timber produced by harvesters. Forests 11(6). https://doi.org/10.3390/F11060626 Steele PH (1984) Factors Determining Lumber Recovery in Saw miI ling Steele PH, Wagner FG, Kumar L, Araman Pa (1993) The value versus volume yield problem for live-sawn hardwood sawlogs. For Prod J 43(April):35–40 Taube P, Orlowski KA, Chuchala D (2020) The effect of log sorting strategy on the forecasted lumber value after sawing Pine wood. Acta Facultatis Xylologiae 62(1):15. https://doi.org/10.17423/afx.2020.62.1.08 Todoroki C, Ronnqvist E (1999) Combined primary and secondary log breakdown optimization. J Oper Res Soc 50(3):219–229. https://doi.org/10.1057/palgrave.jors.2601409 Vergara FP, Palma CD, Sepúlveda H (2015) A comparison of optimization models for lumber production planning. Bosque 36(2):239–246. https://doi.org/10.4067/S0717-92002015000200009 Walker JCF (2006) Primary wood processing: Principles and practice (2nd editio). Springer ILLUSTRATIONS Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4943760","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":342581431,"identity":"3521438b-fca2-4a4a-9631-ee12cf98fed8","order_by":0,"name":"John Ngobi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYHACNoYEHjYeNmbmA0COhAzRWuT42dkSQFp4iNMCBMaS/TwGIAZhLeb8i489eCDDl7jhMM/nVzdqLHgY2A8f3YBPi+WMZ+kGQIcBtfBus845BnQYT1raDXxaDG6cMZOAaTHOYQNqkeAxI1YLzzPjnH/EaDnfA9ZiLNnMw/w4t40oW9jAfpHjZ2YzY87tkwBGESG/nD987OHPnmM8bPyHH3/O+VYHjKDDx/BqYZBIYGBg7DkGYrJJgEm8ykGA/wCQ+FEDYjJ/IKh6FIyCUTAKRiQAAIM6RNTy8AfXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0001-0635-6740","institution":"Busoga Forestry Company","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"","lastName":"Ngobi","suffix":""},{"id":342581432,"identity":"3d9be78d-314c-4162-80f8-1c0d6430ab60","order_by":1,"name":"Robert Kyeyune Kambugu","email":"","orcid":"https://orcid.org/0000-0002-3584-8250","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"Kyeyune","lastName":"Kambugu","suffix":""},{"id":342581433,"identity":"5eeaadf8-0b6b-40f4-a7f2-17a8d2dcbeec","order_by":2,"name":"Paul Mugabi","email":"","orcid":"https://orcid.org/0000-0001-7797-6691","institution":"Makerere University","correspondingAuthor":false,"prefix":"","firstName":"Paul","middleName":"","lastName":"Mugabi","suffix":""},{"id":342581434,"identity":"c6d4fceb-06a2-49be-b4cf-e8df4e94582d","order_by":3,"name":"Abwoli Yabezi Banana","email":"","orcid":"https://orcid.org/0000-0001-9557-5939","institution":"Makerer University","correspondingAuthor":false,"prefix":"","firstName":"Abwoli","middleName":"Yabezi","lastName":"Banana","suffix":""}],"badges":[],"createdAt":"2024-08-20 09:23:32","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4943760/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4943760/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62928533,"identity":"2d6fd55f-cb69-4adc-a7e5-346456ec0d71","added_by":"auto","created_at":"2024-08-21 07:21:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":134283,"visible":true,"origin":"","legend":"\u003cp\u003emap showing study area\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4943760/v1/f97f7d9d8e5425b67297fbb1.png"},{"id":62929164,"identity":"b130293d-2959-44ea-93f4-4c4ecb0fbe96","added_by":"auto","created_at":"2024-08-21 07:29:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":794575,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4943760/v1/7fb530fd-b2e8-45dd-8350-0eeb68f5c7c0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003ePerformance of softwood plantation sawmills: the volume vs. value sawing strategy\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSawmills aim at maximizing profits made from the production and sale of sawn timber and other by-products including slabs, saw dusts or wood chips (Lundahl and Gronlund \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). To achieve this objective, sawmills need to be efficient i.e., effectively utilize manpower, machine and raw material (Lundahl \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). According to Quebec et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), three key performance indicators must be optimized for sawmills to remain competitive and profitable: the volume of logs milled per productive hour relative to the sawmill capacity i.e. log throughput; the volume of sawn timber produced per unit volume of logs milled i.e. volume recovery; and the monetary value of sawn timber produced per unit volume of logs milled i.e. value recovery.\u003c/p\u003e \u003cp\u003eMaximizing log throughput requires a sawyer to properly plan and eliminate bottlenecks in the milling process, reduce machine breakdown through preventative maintenance and effectively manage labor Nwanya et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This eventually results into effective utilization of both machinery and human resources and increases profit margins by reducing the milling costs (Missanjo and Magodi \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Maximizing timber volume recovery is concerned with optimizing sawmill profits by recovering more volumes of sawn timber. This is important since sawn timber is the most valuable and saleable sawmill product (Taube et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, increasing timber volume recovery reduces the unit cost of sawlogs and thus the overall milling costs (Rawat et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, the unit sawlog cost for a sawmill incurring a milling cost of 30 USD/m\u003csup\u003e3\u003c/sup\u003e at 30% can be halved to 50 USD/m\u003csup\u003e3\u003c/sup\u003e when timber volume recovery is doubled to 60%. On the other hand, timber value recovery is concerned with maximizing profits by sawing the more valuable sawn timber grades (Walker \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). According to Mendes and Pasiecznik (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), this is important since sawn timber volume might not reflect the true monetary value of such timber on the market. For example, a roughly sawn timber piece of 8x1x14 will likely fetch a premium price than a 4x2x14 piece even though they have equal volumes. Therefore, sawyers ought to put into consideration the monetary value attached to each timber size while selecting the sawing pattern to use on a particular log.\u003c/p\u003e \u003cp\u003eIncidentally, it is practically impossible to simultaneously maximize all the performance indicators in the real sawmilling environment (Steele et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Todoroki and Ronnqvist \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Lindner and Wessels \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Vergara et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This is because the sawing pattern that maximizes throughput may not maximize the resultant volume or value of sawn timber produced. A study by Steele et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) indicated that maximizing timber volume recovery would reduce the timber value by 2 USD per log sawn by the sawmill whereas maximizing timber value would reduce timber volume recovery by 2%. Nordmark (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) reported a 3% drop in timber volume recovery if a timber value sawing strategy was adopted.\u003c/p\u003e \u003cp\u003eWhile the volume sawing strategy is the most common sawing strategy used by sawmills (Missanjo and Magodi \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ngobi \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), it is not evident that it is better than the value sawing strategy in ensuring the overall competitiveness and profitability of sawmills (Quebec et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Taube et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study determined the performance of sawmills under the timber volume and timber value sawing strategies. This was required as a basis for determining which of the two sawing strategies can better optimize the net revenues from sale of sawn timber by the sawmills.\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Eastern, Central and Albertine plantation clusters in Mayuge, Mubende, Hoima and Masindi districts respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSampling\u003c/h2\u003e \u003cp\u003ePlantation clusters were selected purposively based on accessibility and potential number of sawmills. Sawmills were also purposively selected to include different technologies. Logs were selected systematically to obtain a sample (N\u003csub\u003ei\u003c/sub\u003e) of at least 90 logs in three days for each sawmill (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003esampling intensities used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample sawmill\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003csub\u003ed\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003csub\u003eq\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003csub\u003el\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium band sawmill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile band sawmill-A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile band sawmill-B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMobile circular sawmill\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSampling interval (k) was determined using Equation i. The starting sample log was the first log on the sawmill log deck for the day.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:k=\\frac{{N}_{q}*{N}_{d}}{{N}_{l}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[i\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: N\u003csub\u003eq\u003c/sub\u003e = Average number of logs sawn at sawmill per day as obtained from the sawyers. N\u003csub\u003ed\u003c/sub\u003e = Target number of days to be spent at a sawmill which was 3 given resource constraints, N\u003csub\u003el\u003c/sub\u003e = Number of logs to be sampled (\u0026ge;\u0026thinsp;90).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eFor each sampled log, bark thickness and log length were measured using a measuring tape. The small and butt end diameters were measured using a caliper. Thickness of sawblades used at each sample sawmill was measured using a caliper. The unit price of each timber size produced and the sawing method were also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eFor each log, timber volume recovery and value recovery of each applicable sawing pattern were calculated using equations ii and iii respectively.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{T}_{y}=\\:\\frac{{P}_{y}}{{V}_{l}}\\times\\:100\\%\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[ii\\right]\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{R}_{y}=\\:\\frac{\\sum\\:_{i}{P}_{i}{N}_{iy}}{{V}_{l}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[iii\\right]\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: T\u003csub\u003ey\u003c/sub\u003e = timber volume recovery obtained from a log using sawing pattern y, R\u003csub\u003ey\u003c/sub\u003e = timber value recovery obtained from a log sawn using sawing pattern y, V\u003csub\u003el\u003c/sub\u003e = log volume (m\u003csup\u003e3\u003c/sup\u003e) obtained from log volume table of Pinus caribaea based on small end diameter and length, P\u003csub\u003ey\u003c/sub\u003e = timber volume (m\u003csup\u003e3\u003c/sup\u003e) produced from a log using sawing pattern y, P\u003csub\u003ei\u003c/sub\u003e = price per timber piece (UGX) of size i, N\u003csub\u003ei\u003c/sub\u003e = number of pieces of timber of size i produced using sawing pattern y.\u003c/p\u003e \u003cp\u003eTimber volume produced from a log using a given sawing pattern was calculated from Equation iv.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{P}_{y}=\\:\\frac{\\sum\\:_{i}{n}_{i}{w}_{i}{h}_{i}{l}_{i}}{Q}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[iv\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: n\u003csub\u003ei\u003c/sub\u003e= number of pieces of timber of size i sawn, w\u003csub\u003ei\u003c/sub\u003e = nominal width (mm) of timber of size i sawn, h\u003csub\u003ei\u003c/sub\u003e = nominal thickness (mm) of timber of size i sawn, l\u003csub\u003ei\u003c/sub\u003e = length (mm) of timber of size i sawn, Q\u0026thinsp;=\u0026thinsp;conversion factor (1x10\u003csup\u003e6\u003c/sup\u003e) from mm\u003csup\u003e3\u003c/sup\u003e to m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA sawing pattern was a combination of timber size as center piece/s and any corresponding extractable timber as either side, top, or bottom pieces or a combination of them. For each log, possible sawing patterns were developed following the sawing method (cant sawing) used by the study sawmill. The study adapted and/or modified mathematical algorithms from Maness and Adams (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and Ngobi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and developed sawing patterns in three steps indicated below.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 1: Determining the maximum number of center pieces.\u003c/b\u003e For each sample log, the length (C\u003csub\u003el\u003c/sub\u003e), width (C\u003csub\u003ew\u003c/sub\u003e) and height (C\u003csub\u003eh\u003c/sub\u003e) of possible center pieces that could be extracted were subjected to constraints in equations v, vi and vii respectively.\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{C}_{l}\\:\\le\\:l\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[v\\right]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{C}_{w}\u0026lt;sed\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[vi\\right]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:{C}_{h}\\le\\:2{\\{{r}^{2}-{\\left(0.5\\times\\:{C}_{w}\\right)}^{2}\\}}^{0.5}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[vii\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: l\u0026thinsp;=\u0026thinsp;log length, sed\u0026thinsp;=\u0026thinsp;log small end diameter under the bark, r\u0026thinsp;=\u0026thinsp;radius of log small end diameter under the bark.\u003c/p\u003e \u003cp\u003eThe length of the cant/center piece had to be at most equal to the log length (Equation v) whereas the width must be less than the small end diameter of the log under the bark (Equation vi). Equation vii was formulated by Maness and Adams (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) using Pythagorean Theorem and required that the corresponding thickness of the cant be at most equal to the highest rectangle of width (C\u003csub\u003ew\u003c/sub\u003e ) that can fit into a circle repsenting the small end dimeter under the bark (sed).\u003c/p\u003e \u003cp\u003eSmall end diameter under the bark was obtained from Equation viii as in Sedm\u0026iacute;kov\u0026aacute; et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:sed=\\:\\:\\:\\frac{{sed}_{1}\\:\\:-2*b\\:}{c}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[viii\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: sed\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;log small end diameter (cm) measured over the bark, c\u0026thinsp;=\u0026thinsp;conversion factor from cm to mm. Radius of log small end diameter under the bark was obtained by halving sed.\u003c/p\u003e \u003cp\u003eFor each timber size that could be extracted as center piece, the maximum number of pieces were obtained from Equation ix as in Maness and Adams (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:n=\\:\\:\\:\\frac{2{\\{{r}^{2}-{\\left(0.5\\times\\:cw\\right)}^{2}\\}}^{0.5}\\:+s}{ch+s}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[ix\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: s\u0026thinsp;=\u0026thinsp;kerf width (mm), n\u0026thinsp;=\u0026thinsp;maximum number of center pieces of timber with width (cw) and thickness (ch). n was rounded off, down to the nearest whole number when found to a floating figure since the number of timber pieces must be an integer.\u003c/p\u003e \u003cp\u003eKerf width was obtained from Equation x as in Ngobi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$\\:s=b+2\\times\\:s\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[x\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: b\u0026thinsp;=\u0026thinsp;blade thickness (mm), s\u0026thinsp;=\u0026thinsp;blade setting (mm)\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 2: Determining possible side pieces to be extracted.\u003c/b\u003e The study assumed minimal log eccentricity so that the two resulting side slabs after extracting the center piece/s were of equal size. The timber size that could be extracted from the side slabs as side piece/s was constrained by its thickness (S\u003csub\u003eh\u003c/sub\u003e) and width (S\u003csub\u003ew\u003c/sub\u003e), width of the center piece (c\u003csub\u003ew\u003c/sub\u003e) and radius of small log end diameter (r) as in equation xi.\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\:{{\\left\\{\\right(0.5\\:\\times\\:cw\\:+p*s\\:+{S}_{{h}_{r-1}\\:\\:}+{S}_{{h}_{r}\\:\\:}\\:)}^{2}\\:+{\\left(0.5\\times\\:\\:{S}_{{w}_{r}}\\right)}^{2}\\}}^{0.5\\:}\u0026lt;r\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xi\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ep represented the position of side piece i.e., 1 for inner most side piece extracted next to center piece, 2 for second sidepiece extracted after the inner most piece, 3 for third sidepiece et-cetera. The physical meaning of Equation xi was explained in Ngobi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStep 3: Determining possible top and bottom pieces in each sawing pattern.\u003c/b\u003e Timber pieces which were narrower and/or thinner than centers pieces and could be extracted from the top and bottom slabs as top and bottom pieces were subjected to similar constraints as side pieces. However, the planes of center pieces were reversed as explained in Ngobi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and indicated in equation xii.\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$\\:{{\\left\\{\\right(0.5*(n+\\left(n-1\\right)*s)+r*s\\:+\\:{tb}_{{w}_{r-1}\\:\\:}+{tb}_{{w}_{r}\\:\\:})}^{2}\\:+{(0.5\\times\\:{tb}_{{h}_{r}})}^{2}\\}}^{0.5\\:}\u0026lt;R\\:\\:\\:\\:\\:\\:\\:\\:\\left[xii\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: tb\u003csub\u003ew\u003c/sub\u003e = width of top/bottom piece, tb\u003csub\u003eh\u003c/sub\u003e= thickness of top/bottom piece, r-1\u0026thinsp;=\u0026thinsp;preceding top/bottom piece if any.\u003c/p\u003e \u003cp\u003ePHP programming language was used to code the mathematical algorithm and generate possible sawing patterns for each sampled log. For each log, the sawing pattern that yielded the highest timber volume recovery (T\u003csub\u003emax\u003c/sub\u003e) from equation ii above was identified. This was the volume pattern and the resulting volume recovery was the recovery considered under the volume sawing strategy. The corresponding value recovery (R\u003csub\u003ea\u003c/sub\u003e) of volume pattern was obtained using equation xiii.\u003cdiv id=\"Equm\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e\n$$\\:{R}_{a}=\\:\\frac{\\sum\\:_{i}{P}_{i}{N}_{ia}}{{V}_{l}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xiii\\right]\\:\\:\\:\\:\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: N\u003csub\u003eia\u003c/sub\u003e = number of timber of size i produced using the volume pattern.\u003c/p\u003e \u003cp\u003eOn the other hand, the sawing pattern that yielded the highest timber value recovery (R\u003csub\u003emax\u003c/sub\u003e) as obtained in equation iii above was the value pattern and was considered under the value sawing strategy. The corresponding volume recovery (T\u003csub\u003ea\u003c/sub\u003e) of the value pattern was obtained using equation xiv.\u003cdiv id=\"Equn\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e\n$$\\:{T}_{a}=\\:\\frac{{P}_{a}}{{V}_{l}}\\times\\:100\\%\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xiv\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: P\u003csub\u003ea\u003c/sub\u003e = timber volume yielded by the value pattern.\u003c/p\u003e \u003cp\u003eCluster analysis was used to group logs into classes based on small end diameter. For each log class, timber volume recovery (T\u003csub\u003er\u003c/sub\u003e) and value recovery (R\u003csub\u003er\u003c/sub\u003e) under the volume sawing strategy were calculated using equations xv and xvi respectively.\u003cdiv id=\"Equo\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equo\" name=\"EquationSource\"\u003e\n$$\\:{T}_{r}=\\:\\:\\:\\:\\:\\:\\:\\:\\:\\frac{\\sum\\:{T}_{max}}{{N}_{r}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xv\\right]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equp\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equp\" name=\"EquationSource\"\u003e\n$$\\:{R}_{r}\\:=\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\frac{\\sum\\:{R}_{av}}{{N}_{r}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xvi\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: N\u003csub\u003er\u003c/sub\u003e = number of logs in the log class r, R\u003csub\u003eav\u003c/sub\u003e = value recovery of volume pattern for log v in class r.\u003c/p\u003e \u003cp\u003eTimber value recovery and volume recovery under the value sawing strategy were also calculated using a similar approach as in equations xv and xvi respectively. The weighted timber volume recovery (T\u003csub\u003es\u003c/sub\u003e) and value recovery (R\u003csub\u003es\u003c/sub\u003e) of each sawmill under the volume sawing strategy were calculated using equations xvii and xviii respectively.\u003cdiv id=\"Equq\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equq\" name=\"EquationSource\"\u003e\n$$\\:{T}_{s}=\\:\\:\\sum\\:_{r}{T}_{r}*\\frac{{V}_{r}}{V}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xvii\\right]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equr\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equr\" name=\"EquationSource\"\u003e\n$$\\:{R}_{s}=\\:\\:\\sum\\:_{r}{R}_{r}*\\frac{{V}_{r}}{V}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left[xviii\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere: V\u003csub\u003er\u003c/sub\u003e= Total volume of logs (m\u003csup\u003e3\u003c/sup\u003e) in log class r, V\u0026thinsp;=\u0026thinsp;Total volume of logs (m\u003csup\u003e3\u003c/sup\u003e) sampled at the sawmill. The weighted timber volume and value recovery of each sawmill under the value sawing strategy were calculated using a similar approach as in equations and xvii and xviii respectively.\u003c/p\u003e \u003cp\u003eThe difference in timber volume recovery between the volume and value sawing strategy was tested using a paired t-test at 5% significance level. A paired t-test was also used to test the difference in timber value recovery between the volume and value sawing strategy.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDiameter characterization of logs\u003c/h2\u003e \u003cp\u003eLog small end diameter ranged from 12 cm to 32cm with a mean of 20cm. Medium band sawmill sawed logs with larger diameters relative to other sawmills. The mobile circular sawmill sawed logs with the smallest diameter (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of logs sawn by sampled sawmills\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eSawmill category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eLog diameter class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWeighted Average\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10-19cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20-25cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;26cm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e1\u0026thinsp;=\u0026thinsp;Medium band sawmill, 2\u0026thinsp;=\u0026thinsp;Mobile band sawmill-A, 3\u0026thinsp;=\u0026thinsp;Mobile band sawmill-B, 4\u0026thinsp;=\u0026thinsp;Mobile circular sawmill.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTimber volume recovery\u003c/h2\u003e \u003cp\u003eVolume sawing strategy yielded higher timber volume recovery than the value sawing strategy except for smaller logs (10cm-19cm). The medium band sawmill had the highest volume recovery under both strategies whereas the mobile circular sawmill had the lowest volume recovery. Timber volume recovery increased by 3%, 2% and 1% with the volume sawing strategy for the medium band sawmill, mobile band sawmill and mobile circular sawmill respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eTimber volume recovery for volume strategy (V) and value strategy (R)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSawmill type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10-19cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20-25cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;26cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eWeighted Average\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e1\u0026thinsp;=\u0026thinsp;Medium band sawmill, 2\u0026thinsp;=\u0026thinsp;Mobile band sawmill-A, 3\u0026thinsp;=\u0026thinsp;Mobile band sawmill-B, 4\u0026thinsp;=\u0026thinsp;Mobile circular sawmill.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA paired t-test indicated a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in timber volume recovery between sawing strategies. Volume recovery was significantly different for all log classes at all sawmills except for smaller logs with 10cm-19cm. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePaired samples test on volume recovery between volume and value strategy\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSawmill category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLog class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% C. I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.682.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e1\u0026thinsp;=\u0026thinsp;Medium band sawmill, 2\u0026thinsp;=\u0026thinsp;Mobile band sawmill-A, 3\u0026thinsp;=\u0026thinsp;Mobile band sawmill-B, 4\u0026thinsp;=\u0026thinsp;Mobile circular sawmill.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eTimber value recovery\u003c/h2\u003e \u003cp\u003eValue sawing strategy was associated with higher timber value recovery than the volume sawing strategy for all log classes at all sawmills. Larger logs had the highest value recovery under value sawing strategy (278,000shs/m\u003csup\u003e3\u003c/sup\u003e) whereas smaller logs had the lowest recovery of 161,000shs/m\u003csup\u003e3\u003c/sup\u003e. The respective value recovery rates under the volume sawing strategy were 229,000shs/m\u003csup\u003e3\u003c/sup\u003e and 155,000shs/m\u003csup\u003e3\u003c/sup\u003e. The medium band sawmill had the highest value recovery for all log classes under both strategies whereas the mobile circular sawmill had the lowest value recovery (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eValue recovery (1000shs/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) for volume strategy (V) and value strategy (R)\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSawmill category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e10-19cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e20-25cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;26cm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eWeighted Average\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e377\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e151\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e1UGX\u0026thinsp;=\u0026thinsp;0.00026USD, 1\u0026thinsp;=\u0026thinsp;Medium band sawmill, 2\u0026thinsp;=\u0026thinsp;Mobile band sawmill-A, 3\u0026thinsp;=\u0026thinsp;Mobile band sawmill-B, 4\u0026thinsp;=\u0026thinsp;Mobile circular sawmill.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was a significant difference (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in timber value recovery between the sawing strategies for all log sizes at all sawmills except smaller logs with diameter below 20cmm (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003ePaired samples test on timber value recovery between sawing strategies\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSawmill category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLog class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95% C. I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig (2-tailed)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-21,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-13,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-73,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-81,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-66,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-16,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-58,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-89,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-27,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-16,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-9,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-8.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-58,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14,400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-89,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-27,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-8,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-11,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-6.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-13,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-16,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-11,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e\u0026ge;\u0026thinsp;26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1,800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-4.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e1\u0026thinsp;=\u0026thinsp;Medium band sawmill, 2\u0026thinsp;=\u0026thinsp;Mobile band sawmill-A, 3\u0026thinsp;=\u0026thinsp;Mobile band sawmill-B, 4\u0026thinsp;=\u0026thinsp;Mobile circular sawmill\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe volume sawing strategy yielded higher timber volume recovery than the value sawing strategy in medium and larger logs implying that the sawing patterns that maximized timber volume recovery did not yield maximum timber value recovery for such logs. Similar results were also obtained in Steele et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), Todoroki and Ronnqvist (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and Nordmark (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The lack of difference in timber volume recovery between the sawing strategies for the smaller logs can be attributed to limited number of possible timber sizes that can be extracted from such logs and thus fewer applicable sawing patterns.\u003c/p\u003e \u003cp\u003eUnder the volume sawing strategy, timber volume recovery increased with log sizes. Increase in timber volume recovery with log diameter has also been reported in Kambugu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), Missanjo and Magodi (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Ngobi (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and holds if the larger trees, which are generally older are harvested while still with large volumes of sound wood (Steele, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). The medium band sawmill had the highest timber volume recovery for each log size and this can be attributed to the relatively larger diameter logs that were sawn by the sawmill. Additionally, the medium band sawmill was equipped with optimizing edgers and resaws and recovered narrower, thinner and/or shorter sawn timber pieces. The mobile circular sawmill had the lowest timber volume recovery due to the relatively small diameter log sizes that it sawed. Moreover, the mobile circular sawmill had the thickest saw blade which resulted into a wide saw-kerf and consequently, conversion of larger volumes of logs into saw-dust. According to Kambugu et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), sawmills with thick saw blades are inefficient and inappropriate for use in conversion of small diameter logs.\u003c/p\u003e \u003cp\u003eAssuming an harvesting and milling cost of 150,000UGX/m\u003csup\u003e3\u003c/sup\u003e (FAO, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), zero cost for saw logs since the sawmills owned the forest plantations, and timber value recovery obtained in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e under the volume sawing strategy, the net revenue from sale of sawn timber for each respective log size were 99,000UGX. 143,000UGX and 206,000UGX for the medium band sawmill and \u0026minus;\u0026thinsp;41,000 UGX, -16,000 UGX and \u0026minus;\u0026thinsp;7,000 UGX for the mobile circular sawmill. Therefore, the mobile circular sawmill did not realize profits from sale of sawn timber for all the log diameter sizes. On the other hand, the medium band sawmill was profitable even with no consideration of revenue obtained from sale of by-products. Mobile band sawmills which had relatively higher timber volume recovery than the mobile circular sawmill only realized a positive net revenue from medium and larger log diameter sizes i.e., -14,500 UGX, 8,500 UGX and 58,500 UGX for smaller, medium and larger log sizes respectively.\u003c/p\u003e \u003cp\u003eTimber value recovery obtained under the value sawing strategy was higher than that of the volume sawing strategy except for the small log sizes.Therefore, log sawing patterns that maximized timber value recovery did not maximize timber volume recovery in medium and large logs. Consequently, the value sawing strategy maximized timber value recovery at the expense of timber volume recovery except in these two log sizes. The medium band sawmill had the highest timber value recovery under the value sawing strategy which can be attributed to the relatively higher volume of timber recovered from the logs and the higher prices attached to the sawn timber produced. Compared to the volume sawing strategy, timber value recovery increased by 1%, 3% and 21% for the respective log sizes when the value sawing strategy was adopted at the medium band sawmill. This is generally higher than the 3% increase in timber value recovery reported in Todoroki and Ronnqvist (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) when the value sawing strategy was considered. The net revenue from the sale of sawn timber for the respective log sizes increased by 2%, 6% and 37% at the medium band sawmill. Although the net revenue from the sale of sawn timber increased for the mobile circular sawmill by 24%, 62% and 43% for the respective log sizes, the sawmill still had a negative net revenue from sale of sawn timber under the value sawing strategy. The mobile band sawmills had their net revenue increased by 10%, 70% and 32% for the respective log sizes. The lower increase in net revenues obtained from larger logs when the value strategy was adopted at the mobile circular and band sawmill can be attributed to the fact that timber pricing is not only dictated by the corresponding timber volume but also the prevailing timber demand, log requirement, present stock and production costs (Kant, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). On the other hand, the practice of attaching premium prices on wider timber pieces milled from only larger logs might explain the relatively higher increase in net revenue associated with the larger log sizes at the medium band sawmill.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThe volume sawing strategy increased timber volume recovery in the medium (1%) and larger logs (2%) but did not affect timber volume recovery in smaller logs at all sawmills. The highest and lowest increment were obtained at the medium band sawmill and mobile circular sawmill respectively. The corresponding timber value recovery declined by 3%, 6% and 17% in smaller, medium and larger logs.\u003c/p\u003e \u003cp\u003eTimber volume recovery dropped with the value sawing strategy but the timber value recovery was maximized at 161, 000UGX 195, 000UGX and 278, 000UGX for smaller, medium and larger logs. The net revenue from sale of sawn timber from the respective log sizes increased by 120%, 32% and 62% with adoption of the value sawing strategy.\u003c/p\u003e \u003cp\u003eThere is a need to study the effect of the two sawing strategies on log throughput for the different sawmills.\u003c/p\u003e \u003cp\u003eThe value sawing strategy should be adopted for all log sizes at all sawmills since it indicated potential for improving the overall profitability of the sawmills.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eACKNOWLEDGMENT\u003c/h2\u003e \u003cp\u003eThis study was funded by the Carnegie Corporation through Makerere University, Directorate of Research and Graduate Training under the Supporting Early-Career Academics (SECA-2019) Project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO (2020) \u003cem\u003eUnlocking future investments in Uganda\u0026rsquo;s commercial forest sector\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohansson M (2007) Product Costing for Sawmill Business Management. 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Springer\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eILLUSTRATIONS\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"00d3bfc7-66db-41c5-9372-0fa05422bf3e","identifier":"10.13039/501100009914","name":"Makerere University","awardNumber":"N/A","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Makerere University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sawing strategy, Volume sawing strategy, Value sawing strategy, Timber volume recovery, Timber value recovery, log throughput","lastPublishedDoi":"10.21203/rs.3.rs-4943760/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4943760/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSawmill performance is anchored on three indicators: timber volume recovery, timber value recovery and log throughput. Traditionally, sawyers use the volume sawing strategy aimed at maximizing timber volume recovery. The question is, does the volume sawing strategy result into better performance or an alternative strategy or a hybrid of strategies would yield better results. To answer this question, this study determined timber volume and timber value recovery under the volume and value sawing strategy. Data were collected from logs randomly selected from four sawmills and grouped using cluster analysis. PHP programming language was used to determine sawing patterns that maximized timber volume and/or value from each log. The difference in timber volume and value recovery between the volume and value sawing strategy was tested using a paired t-test at 5% significance level. The value sawing strategy yielded significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher timber volume than the value sawing strategy except for in smaller logs (10-20cm). Timber value recovery was significantly higher (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) under the value sawing strategy than volume sawing strategy for all log sizes. Mean reduction in timber volume recovery was 2% whereas the increment in timber value recovery was 12% under the value sawing strategy. Adoption of the value sawing strategy by the sawmills was recommended since it indicated a potential for improved sawmill profitability.\u003c/p\u003e","manuscriptTitle":"Performance of softwood plantation sawmills: the volume vs. value sawing strategy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-21 07:21:24","doi":"10.21203/rs.3.rs-4943760/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e9b3d000-ce3a-40c1-aac6-e78044a63e75","owner":[],"postedDate":"August 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":36292119,"name":"Forestry"}],"tags":[],"updatedAt":"2024-08-21T07:21:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-21 07:21:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4943760","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4943760","identity":"rs-4943760","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}