Analysis of Implementation of Best Management Practices in Oil Palm Plantations in Indonesia

Transcription

1 Analysis of Implementation of Best Management Practices in Oil Palm Plantations in Indonesia Tiemen Rhebergen MSc thesis Plant Production Systems May 2012

2 Analysis of Implementation of Best Management Practices in Oil Palm Plantations in Indonesia Tiemen Rhebergen MSc thesis Plant Production Systems PPS May 2012 Supervisors: Prof. Dr. KE Giller; Plant Production Systems, Wageningen University, Wageningen, Netherlands Dr. T Oberthür; IPNI Southeast Asia Program, Penang, Malaysia Examiner: Dr. Katrien Descheemaeker Prof. Dr. KE Giller

3 Contents Abstract... 4 Introduction... 5 I. Background & Problem statement... 5 II. Research objectives & hypothesis... 6 III. Concepts... 6 Yield gap... 6 BMP as a management tool... 7 Material & Methods... 8 I. Area and site description... 8 II. Experimental design... 9 III. Data collection & management IV. Data analysis i. Analysis ii. Analysis Results I. Analysis i. Between treatments ii. Between treatments within and between sites iii. Between treatments within and between estates iv. Between treatments within and between climatic zones v. Between treatments within and between soil textures vi. Between treatments within and between islands vii. Between treatments within and between (site-) conditions II. Analysis i. Site (Plantation) level ii. Estate level Discussion & Conclusions Acknowledgements References

4 Abstract With an increasing world population and a growing demand for palm oil, an extra 12M ha will be needed over the next 40 years. The growing demand can be met by improving yields in existing oil palm plantations through Best Management Practices (BMPs) by using cost-effective and practical agronomic methods. The main objective of this research is to analyse BMP implementation and the main drivers of yield intensification across and within six sites encompassing a wide range of environments that represent major production areas in Indonesia. Results of the BMP implementation were compared to standard commercial practices (REF), and showed that average yields in BMP were greater compared with REF blocks. This indicates that BMP significantly closes yield gaps in mature oil palm plantations and proves the immense potential for increasing yields in existing plantations in Indonesia. BMP implementation was highly variable across a wide range of environments, indicating that the BMP concept is site-specific and applicable across a wide range of conditions. Further analysis showed that environmental, management and genetic factors influence yield variability. In particular, where management practices such as crop recovery were not carried out properly in REF treatments, this had a greater effect on yield. Implementing BMP will benefit the palm oil industry in the short term through operational management such as crop recovery and in the long term through better site-specific agronomic management. It is advised that plantations monitor and evaluate environmental conditions before implementing site-specific BMP s. When implemented correctly to site-specific conditions, productivity in existing plantations can be increased.

5 Introduction I. Background & Problem statement Oil palm (Elaeis guineensis) accounts for nearly 30% of global vegetable oil production [2] and is an important driver in the economic development of many tropical countries [12]. It is a very efficient crop in terms of input utilization [3] and produces the highest oil yield compared with other oil producing crops [5]. Southeast Asia is the largest producer of palm oil [8], where planted area and production of palm oil has increased exponentially since the 1970s [4]. During the past decade, the most significant increases in production have occurred in Indonesia and Malaysia, which now account for around 85% of global palm oil production [6]. However, to keep up with an increasing world population and a growing demand for palm oil, an extra 12M ha will be needed over the next 40 years [2]. If all production were to take place in Indonesia and Malaysia, more than a 100% increase in area under production is required [12]. Whilst area under production has increased rapidly during the past 40 years, average achieved oil yields in Indonesia and Malaysia have remained far below potential levels [4]. Area expansion raises concern on environmental impacts such as forest destruction and loss of biodiversity [12]. It has therefore been proposed to expand oil palm production into so-called degraded lands to reduce pressure on forest reserves [12]. Because of less favourable conditions in terms of resource quality and infrastructure lower site yield potentials and higher production costs per unit yield occur on these degraded lands [18]. Alternatively, increasing productivity in already existing plantations offers scope for improvement and reduces the need to increase area for oil production. Financial returns through yield intensification are expected to be larger because there is no need to invest in new plantings and plantation infrastructure. In addition, financial returns are expected to develop more rapidly, because production starts to increase as soon as agronomic constraints are removed [4]. Increases in production can be met through intensification by improving yields per area and/or oil extraction rates. Best Management Practices (BMPs) developed by The International Plant Nutrition Institute s (IPNI) Southeast Asia Program (SEAP) focuses on increasing palm oil yield in existing mature plantations by using agronomic methods and techniques that are cost-effective and practical. Yield improvement efforts in existing plantations thereby focuses on identifying and rectifying management practices that contribute to the emergence of a gap between the yield potential and actual achieved yield; the yield gap [4]. The main objective of this research is to analyse BMP implementation and the main drivers of yield intensification across and within six sites that represent major production areas in Indonesia. Understanding the mechanisms and variables regulating yield can help eliminate yield differences inand between sites and enable us to improve crop response to natural variation and variation in management practices. Such an analysis will give insight on the site-specific nature of BMPs and provide the appropriate tools to address them accordingly. The ultimate goal of this study is to contribute evidence and incentives for plantation managers across Southeast Asia to adopt and adapt BMP as a management tool in intensifying oil palm production. 5

6 II. Research objectives & hypothesis Objectives 1) Build a database with data from six sites (plantations) encompassing 18 estates in Indonesia where BMP has been implemented 2) Analyse the yield performance under Best Management Practices (BMP) and Reference Estate Management Practices (REF) at six sites in Indonesia 3) Compare the performance of BMP and REF management practices across six sites in Sumatra against Kalimantan with different agro-ecological potential for oil palm production 4) Develop and implement an approach to identify the contribution of different factors to yield variability Hypothesis 1) Best Management Practices (BMP) significantly close existing yield gaps in mature oil palm plantations across a wide range of environments 2) Yield gap reduction is variable within plantations and across plantations, and thereby leads to site-specific responses to BMP 3) Variability is caused by site-specific environmental, genetic and management factors whose impact on yield gap can be quantified III. Concepts Yield gap Yield gaps (Y g ) are calculated as the difference between the actual achieved yield (Y a ) and the yield potential (or potential yield) (Y p ) for a given crop. The Y P is based on the assessment of site-specific characteristics such as climate and is therefore highly variable across and within regions [20]. Y P is defined as: the yield of a crop cultivar when grown with water and nutrients non-limiting and biotic stress effectively controlled [20]. Yield gap analysis (YGA) is used to identify poorly performing blocks where corrective management can be implemented to increase actual yields. YGA is consequently apportioned into three parts, where: 1) Yield Gap 1 (G1) arises from inefficiencies during the development of a plantation until the end of the immature period 2) Yield Gap 2 (G2) arises due to nutrient constraints in the production phase 3) Yield Gap 3 (G3) arises due to poor harvesting and management in the mature stand While G1 offers limited opportunities for improvement in existing plantations (which occur only at the initial establishment of the plantation and at each occasion of replanting), G2 and G3 can be corrected in existing mature stands by identifying and rectifying management practices that contribute to the emergence of such a gap [8].

7 Because oil palm is a perennial crop, oil palm yields are not only affected by planting material, environment and management (G x E x M), but also by tree age. For most environments in Southeast Asia, yields peak between 7 to 10 years after planting (YAP), after which they decline due to reduced tree stand (pests & disease infestations) and difficulties with harvesting tall palms, resulting is less complete crop recovery [12]. To analyse yield trends accurately, YGA should be performed according to YAP. Furthermore, agronomic data for each block and year of production together with its projected Y P profile is needed. YGA is a powerful tool in the identification, selection and prioritization of best management practices (BMPs) for intensifying yield [12]. BMP as a management tool The International Plant Nutrition Institute s (IPNI) Southeast Asia Program (SEAP) has developed a concept Best Management Practice (BMP) as a management tool for correcting yield gaps (G1, G2) in existing mature stands. The BMP concept is a programme for plantation companies to improve their agronomic performance and accordingly increase profit. Hence, the key-point of the BMP concept is to improve yields by using cost-effective and practical agronomic methods and estate organization and planning [4]. BMP aims to stimulate productivity in the short term without the need to increase in land area and focusses on identifying and rectifying management practices that contribute to yield gaps in existing mature oil palm plantations. The implementation of BMPs benefits palm growth and productivity, but also soil, water and nutrient conservation. Accordingly, BMPs are classified to: crop recovery, canopy management and nutrient management. Table 1 (based on Donough et al. 2010) further specifies BMPs implemented within these categories: Table 1. BMPs implemented at IPNI SEA project sites in Indonesia Crop recovery BMPs Canopy management BMPs Nutrient management BMPs Harvest interval (HI) of 7 days Maintenance of sufficient fronds to support high palm productivity Spreading pruned fronds widely in inter-row area and between palms within rows Minimum ripeness standard (MRS) = 1 Removing abnormal, unproductive Eradication of woody perennial weeds loose fruit (LF) before harvest palms Same day transport of harvested crop In-filling unplanted areas Mulching with empty fruit bunches (EFB) to palm oil mill Harvest audits to monitor completeness of crop recovery and quality (i.e. ripeness) of the harvested crop Selective thinning in dense areas Management of applied fertilizers (i.e. type, dosage, timing and placement) Good in-field accessibility (clear paths, bridges wherever needed) Clean weeded circles Palm platforms constructed and maintained wherever needed Minimum under-pruning in tall palms to ensure crop visibility Monitoring and management of pests (leaf eaters) and disease (Ganoderma) Monitoring of plant nutrient status and growth IPNI SEA currently aims to optimize BMP by means of operational research based on commercial data, which provides insight on yield response under variable environments and management and will allow the results to be scaled up to commercial levels. Commercial data provided by the estates, including biophysical variables and management practices over a range of conditions can be used to obtain valuable insights on how to better manage crops according to specific environmental conditions, and also avoids the need to establish and manage a large number of costly experiments. By quantifying the factors that impact yield, better site-specific management practices (SSBMPs) can be identified and improved upon in standard commercial practices closing existing yield gaps in 7

8 mature oil palm plantations. This offers decision makers a better understanding of which BMPs should be implemented as a management tool in intensifying oil palm production and enables us to describe the uncertainties that are faced by plantation managers. Material & Methods I. Area and site description Oil palm is grown under highly variable conditions such as climate, different kinds of soil and land suitability classes. Favourable conditions for oil palm production is in areas with an annual rainfall between 2,000-3,500 mm, evenly distributed throughout the year with a minimum of 100 mm per month and an optimum mean annual temperature range of C [18]. Topography and slope are important land characteristics that determine suitability for oil palm production. Decreasing temperatures play a role with increasing elevation, while slope determines the potential for soil erosion and difficulties in establishing and maintaining terraces and the costs associated with harvesting. It is therefore recommended not to plant oil palm >200 m above mean sea levels and on slopes >38% (>20 ). The ideal climatic and topographic conditions for growing oil palm are presented in Table 2 [18]. Table 2. Ideal climatic and topographic conditions for oil palm growth Climate Units Ideal conditions Sunshine hrs d -1 >5.5 Solar radiation MJ m -2 >16 Annual rainfall mm yr -1 2,000-2,500 Monthly rainfall mm month -1 >100 (in all months of the year) Annual water deficit mm <200 Relative humidity % Mean temperature C 28 Mean wind speed m s Topography Slope % As long as there is enough water, oil palm can be grown on a wide range of soils [17], such as Ultisol (Podzolic/Latosol), Entisol (Alluvial), Inceptisol (Latosol/Podzolic), Andisol (Andosol) and Histosol (peat soil or Organosol) [14]. Soil physical properties are thereby considered to be more important than soil chemical properties because of the importance of soil moisture supply. The chemical composition of soils - and nutritional deficiencies within the palm - can easily be corrected by using cost-effective mineral fertilizers, while correcting soil physical properties is a more difficult and costly undertaking [18]. Important soil physical properties are for example soil texture and structure. Soil texture describes the relative amounts of (fine and coarse) sand, silt and clay found within a particular layer, while soil structure describes how these components are aggregated. Sandy, loamy and coarse sandy -textured soils are not desirable for oil palm cultivation because they are susceptible to drought as well as leaching. Poorly-structured and sandy textures can partly be improved by using organic manures and empty fruit bunch (EFB) mulching to improve soil structure, soil moisture, and nutrient retention capacity. Due to its high porosity and therefore its capacity to retain more moisture and nutrients well-structured clay (C), sandy clay (SC), clay loam (CL) and silty clay loam (SiCL) textured soils are ideal for OP cultivation [18] as are sandy clay loam (SCL) and silty loam [14]. Table 3 shows the suitability of soil textures for oil palm production.

9 Table 3. Suitability of soil textures for oil palm production Soil texture Clay loam Coarse sand Loam Massive clay Sand Sandy clay Sandy clay loam Sandy loam Silty clay loam Silty clay Well-structured clay Silty loam Suitability for oil palm production Poor Marginal Good Within Southeast Asia, oil palm is mostly grown between 10 N and 10 S of the equator due to its climatic requirements [8]. The most significant increases in production during the past decade in Southeast Asia have occurred in Malaysia and Indonesia, which together account for an estimated 85% of global palm oil production and 23% of the world oils and fats production [6]. Indonesia, in particular, is ideal for oil palm cultivation because of its favourable climatic and soil conditions [8]. During , mature palm area in Indonesia grew at 10% per annum, while palm oil production increased by 17.4% per annum. In 2006, Indonesia took over Malaysia s position as world s largest palm oil producer (producing an estimated 19.8 M tons by % more than in 2000) and is forecasted to contribute nearly half of the world s total future production [6]. Palm oil is Indonesia s most important agricultural export crop and many oil palm plantations are therefore found located throughout the country in 17 provinces in Sumatra, Java, Kalimantan, Sulawesi, Muluku and Papua [6]. In 2005, the largest plantation in the country was located in Sumatra, with a total size of 4.3 M ha (or 76.5% of the total plantations). In Sumatra, Riau accounted for 1.4 M ha, followed by North Sumatra with 0.96 M ha. Kalimantan has a total of 1.1 M ha (19.8% of the total plantations), with West Kalimantan accounting for 0.47 M ha and Central Kalimantan with 0.27 M ha. Sumatra remains Indonesia s prime oil production region (75% of the total mature palm area and 80% of total palm oil production) and is still expanding with an average increase of 6% for the past ten years. Within this period, plantations also started to expand more into remote areas on Kalimantan, Sulawesi and Papua. II. Experimental design In the experimental design, a parallel set of comparable blocks representative of a plantation are selected. Within the higher yielding block, standard commercial practices are maintained (REF blocks), while a set of SSBMPs are identified and introduced in the lower yielding block of each pair for comparison (Figure 1 [22]). For both fields an inventory of limiting factors is prepared, but only for the BMP block corrective action is taken. 9

10 Figure 1. BMP Experimental block design Since July 2006, 60 paired blocks (total area 2,184 ha) have been selected, with BMPs applied on 30 blocks (total area 1,080 ha). Five plantation groups collaborated on the BMP project at six different locations throughout Indonesia, covering a wide range of environments where oil palm is grown in North and South Sumatra, and West, Central and East Kalimantan (Figure 2 [23]). Figure 2. Locations of the six IPNI SEA BMP project sites in Indonesia Table 4 provides environmental conditions for the IPNI SEA BMP project. Table 4. Environmental conditions at the IPNI SEA BMP project sites in Indonesia Site Soil Type Annual mean Annual mean Topography & Slope (%) rainfall (mm) temperature ( C) 1 Alluvial & Sedentary Level (0-4%) & Undulating (4-12%) 2 Sedentary & Alluvial Level (0-4%), Undulating (4-12%) & Rolling (12-24%) 3 Sedentary Undulating (4-12%) 4 Sedentary Undulating (4-12%) & Rolling (12-24%) 5 Sedentary Level (0-4%) & Undulating (4-12%) 6 Sedentary Rolling (12-24%) & Hilly (24-38%) Average climatic conditions over a period of 30 to 50 years taken from [26] Topography & Slope classification based on Paramananthan, 2003 Per site, five paired blocks of at least 25 ha were selected to represent the estate, while palm stands varied between and within sites. At each site the project started at different times and is run for a

11 total of 4 years, after which BMPs are evaluated. See Table 5 for project information per site and Figure 3 (made with CMAPS) for a hierarchal flow diagram of the project outline. Table 5. Project information for each site in the IPNI SEA BMP project in Indonesia Island Province Site No. Stand (palms ha -1 ) Area (ha) Start Stand Estates BMP REF BMP REF date age (yr.) Sumatra North Sumatra Aug North Sumatra Sep South Sumatra Feb Kalimantan West Kalimantan Mar Central Kalimantan Jun East Kalimantan Jul Figure 3. Flow diagram of the project outline. The numbers at the bottom indicate the number of paired blocks per estate BMPs embedded within commercial operations across a variety of sites and estates will enable us to improve crop response to natural variation and variation in management practices. Understanding site-specific variability can help estates identify and implement particular management practices targeted towards yield intensification. Comparisons between SSBMPs and blocks managed under standard commercial practices will be analysed and used to provide information on how to better manage crop production and guide decisions on a commercial scale, therefore reducing decision uncertainty under plantation managers. III. Data collection & management During the project period, all field activities and data collection was overseen by the local estate managers of IPNI SEAP s project partners [5]. Data was collected in the field by field assistants hired for the BMP program, while the collaborating plantations provided additional data on the project blocks such as area (ha), stand age (yr.), stand densities (palm ha -1 ), planting material, seed source, but also climate data such as rainfall (mm). Annual mean temperatures ( C) for each site was taken from ([26] accessed on ). 11

12 All data was forwarded to the IPNI SEA office, where I re-arranged the data to a single format in MS Excel. Data was separated per Site and organised in columns by Estate, Block ID and Treatment, while each row of data was accordingly lined up to Harvest date - making each single row of data unique. In addition, calculations were carried out to determine Harvest Interval (number of days between two consecutive harvests), Yield components (kg Fresh Fruit Bunches (FFB) ha -1, Number of Bunches ha -1, Average bunch weight (kg)) and Harvester s Productivity (area (ha) Man-day -1, Number of bunches Man-day -1, kg FFB Man-day -1 ). For sites 4 and 6, additional Loose Fruit Collection (LFC) (kg) took place and was added up to kg FFB to determine the total harvest (kg) for a given harvest round. For these 2 sites, yield (FFB ha -1 ) was calculated by taking the total harvest instead of just fruits harvested from the palm as in sites 1-3 & 5. Ganoderma, or Basal stem rot (BSR) is a fungal disease causing severe yield losses through direct loss of the stand and reduced yield of infected palms which are still alive [11]. Site 1 was heavily affected by the disease, so calculations were carried out to correct stand densities due to dead and diseased oil palms. Percentage of coarse sand, fine sand, sand, silt and clay from the soil analysis data was used to calculate soil texture. Measurements for each particle were averaged across samples taken at 2 depths (0-20 and cm) and 2 locations ( weeded circle and frond stack ) from the beginning and end of the project. An average soil texture for each single block was produced by using the USDA Soil Texture Calculator developed by the United States Department of Agriculture, NRCS Natural Resources Conservation Service [25]. Based on a classification of climatic conditions presented in Lubis et al. (1996), five climatic zones of oil palm cultivation is given; Extremely wet, Wet, Slightly wet, Slightly dry and Dry (Table 6). The wet climatic zone is the optimal climate for oil palm growth, where rainfall is equally distributed throughout the year, while the slightly wet zone is still suitable (but where rainfall is not equally distributed). Slightly dry zones are also still possible for oil palm growth, but water deficits of about mm a year may occur and could limit growth and production of oil palms due to low moisture status. Oil palm cultivation in dry climatic zones is not recommended because of very low rainfall and high annual water deficits. Marginal lands for oil palm are thus characterized by a low average rainfall (slightly dry and dry climatic zones) and >2 dry months, while more suitable areas have a higher average rainfall (annual average >2500 mm) [14]. Table 6. Climatic zones of oil palm cultivation based on annual average rainfall (mm), rain days (d -1 ) and dry months (<60 mm) Climatic zone Annual average rainfall (mm) Annual average rainy days (d -1 ) Annual average dry months (<60 mm) Extremely wet >2750 >200 0 Wet Slightly wet Slightly dry Dry <1250 <75 >3 Monthly rainfall data (mm) from was used to calculate confidence limits (with 90% confidence interval) of minimum and maximum expected rainfall for each month for each estate. The average rainfall (mm), number of rain days (d -1 ) and number of dry months (<60 mm rainfall)

13 throughout the entire year was then used to determine which climatic zone represented each estate best. Table 7 gives an overview of climatic zones and soil textures calculated for each estate. Table 7. Climatic zones and soil texture for each site and estate in the IPNI SEA BMP project in Indonesia Site Estate Average Average number Average number of Climatic zone Soil textures rainfall (mm) of rain days (d -1 ) dry months (<60 mm) 1 GB Slightly dry Sandy clay loam KP Slightly wet Sandy clay loam SB Slightly wet Coarse sandy loam, Sandy clay loam SE Slightly dry Sandy clay loam, Coarse sandy loam TR Slightly wet Sandy clay loam 2 BU Extremely wet Sandy clay loam, Sandy clay, Fine sandy loam PAP Extremely wet Coarse sandy loam, Clay AA Extremely wet Sandy clay 3 BK Wet Clay BT Wet Sandy clay, Sandy clay loam, Clay loam, Clay 4 SNB Extremely wet Coarse sand, Fine sand, Fine sandy loam, Loamy coarse sand, Loamy fine sand 5 SS Wet Coarse sandy loam, Loamy coarse sand SU Wet Loamy coarse sand WA Wet Loamy coarse sand Coarse sandy loam 6 CA Extremely wet Clay loam, Sandy clay loam LE Extremely wet Clay loam SEN Extremely wet Sandy clay loam, Clay loam PE Extremely wet Clay loam Land suitability classes for oil palm growth for each site was judged by expert knowledge (Chris Donough) and is based on key site factors impacting yield [5]. Yield potentials for each suitability class were taken off a poster from the Indonesian Oil Palm Research Institute (IOPRI), which was presented at the International Oil Palm Conference, Jogjakarta, Indonesia, Site conditions based on Donough et al. (2010) are presented in Table 8. Table 8. Site conditions in the IPNI SEA BMP project in Indonesia Site Suitability Prior Yield Yield potential Site Site factors class (ton ha -1 ) (ton ha -1 ) conditions 1 S Good Level terrain Low rainfall Ganoderma 2 S Good Rolling terrain Planting material Terrain Variable stand 3 S Moderate Undulating terrain Severe water deficit in many years 4 S2/ Poor Undulating terrain High rainfall Poor soil - sandy 5 S Very poor Poor soil - very sandy Low rainfall Water deficit in some years 6 S Good Rolling terrain Very high rainfall S1 = highly suitable, S2 = moderately suitable, S2/3 = moderate to marginally suitable, S3 = marginally suitable After all the data was gathered, re-arranged and sorted and all the calculations and classifications were done, all the data was compiled and stored in one large dataset in MS Access. 13

14 IV. Data analysis The data analysis was carried out in two parts; Analysis 1 was performed to describe the main features of the BMP and REF yield data across and within sites, while Analysis 2 probed deeper into underlying factors accounting for yield trends between the treatments by taking the site North Sumatra 1 as an example. i. Analysis 1 Analysis 1 focused on Hypothesis 1 & 2 where treatment means were compared across or/and within a variety of groups. Before starting with the statistical analysis (IBM SPSS Statistics 19) data sets for each separate analysis were compiled by selecting relevant data from the MS Access Database. Daily yield data was summarized on a monthly basis and categorized according to project year per site. Project year 1, for example, ran from the first month of BMP implementation for 12 months. Each site was run for 4 project years. Yield data was analysed based on 12 month rolling yield (t ha -1 ), which was calculated by taking the average yield of the first 12 months and then shifting forward, by excluding the first month and including the next, thereby creating a series of successive averages. This process is repeated over the entire data series and is used to decrease the impact of month-to-month or seasonal fluctuations in yield and allow detection of trends in yield. Using this method, no rolling yield values can be calculated for the first 11 months. In addition, actual monthly yields (t ha -1 ) were calculated and analysed as well. In the results section, only rolling yields were used, because more significant differences within and between treatments were found. Furthermore, Figures 6 and 7 illustrate why rolling yields are preferred, as the power of detecting yield-trends in addition to actual yields is much greater. Twelve month rolling yields (12MRY) for both treatments were compared across a variety of groups by using a Two-Way ANOVA (General Linear model, UNIVARIATE) with 12MRY as the Dependent variable. According to which analysis was run, Island, (site-) Conditions, Site, Estate, Treatment, Project year, Climatic zone, Soil texture and Seed source were used as Independent variables (or fixed factors). Custom models were built in the Syntax Editor and run accordingly. Where appropriate, an interaction term was added. Treatment 12MRY means were analysed as the average of all project years together. A filter was added to the model syntax to analyse treatment means for each separate project year as well. Table 9 shows all performed analyses with the accompanying model syntaxes. It should be noted that results of models 5 to 8 (Table 9) could be influenced by a site as a confounding variable, which can adversely affect the relation between yield and the independent variable. For models 1 & 2 (see Table 9), an interaction term (Project_yearTreatment) was added to test if there were any significant differences in yield between consecutive project years and treatment, and treatments within sites respectively. Table 9. Analyses & model syntaxes performed in Analysis 1 Model Analysis of Yield performance (12MRY in ton ha -1 ) Model syntax /DESIGN = 1 between Treatments Site Estate(Site) Treatment 2 between Treatments within Sites Estate Treatment 3 between Treatments between Sites Site Estate(site) Treatment TreatmentSite 4 between Treatments within and between Estates Estate Treatment EstateTreatment 5 between Treatments within and between Climatic zones Clim_zone Site(Clim_zone) Treatment TreatmentClim_zone 6 between Treatments within and between Soil textures Soil_tex Site(Soil_tex) Treatment TreatmentSoil_tex 7 between Treatments within and between Islands Island Site(Island) Treatment TreatmentIsland 8 between Treatments within and between (site-) Conditions Condition Site(Condition) Treatment TreatmentCondition

15 ii. Analysis 2 Analysis 2 focused on answering Hypothesis 3 to identify factors that cause variability in yields. To demonstrate this, a simple approach including partial factor contribution to yield variability was identified. A linear regression was carried out in SPSS to analyse yield trends. Site 1 (North Sumatra 1) was selected to perform the analysis on, because it has the most complete database. The influence of factors on yield can be variable when looking at different spatial scales. To examine this, the analysis was carried out at two different scales: the site (plantation) and estate level. When increasing the scale of analysis, new interactions and relationships may emerge and exhibit patterns that occur at scales specific to those processes. Knowledge on these processes provides us with useful information on the management of factors at different scales [15]. Twelve month rolling yields (12MRY) were used as the dependent factor while a list of independent variables is presented in Table 10. Because there were only five estates, this limited the number of degrees of freedom (d.f.) and in turn the number of independent variables that could be included in the model. Variables that seemed unlikely to be highly correlated with each other were chosen. Table 10. Independent variables at Site- and Estate level used in Analysis 2 Variable Unit/Categories Categorical Numeric Site Estate Treatment 1. BMP 2. REF Topography 1. Flat 2. Flat to undulating 3. Undulating to rolling Seed source 1. Bah Lias (Lonsum) 2. Dami (PNG) 3. Socfindo Soil texture 1. Coarse sandy loam 2. Sandy clay loam Climatic zone 1. Slightly dry 2. Slightly wet Rainfall mm Rain days d -1 Harvest days d -1 Harvest round rounds -1 Man days d -1 Mg kg block -1 N kg block -1 P kg block -1 K kg block -1 Ganoderma Adjusted stand size due to diseased and dead palms: palms ha -1 all units per month Analysis at the site level included a number of categorical variables, such as treatment, climatic zone and soil texture, while analysis at the estate level excludes most of these variables, because within an estate they are constant. Categorical predictor variables cannot be entered directly into a regression model and interpreted meaningfully, so dummy variables have to be created. In this process, categorical variables are transformed into binary variables. The code 1 is thereby indicated for the level of interest and 0 for all other levels. If, for example, of a 2-level categorical variable, one categorical level is presented to be significant in the regression output (e.g. Treatment = REF ), this does not mean that REF is significant, but the effect of treatment as a whole. This is because at least one level will not be accounted for in the regression, as it is automatically identified by the model intercept. 15

16 Transformations of all numeric variables (by taking the square function of itself) were included in the analyses as well to test whether this would improve the linearity of its relation to yield. To keep the analysis simple, no interactions were included in the model. Only main factors and their transformations were included, which should provide enough insight into observed yield trends. A linear regression was carried out for Site 1 and for all individual estates within Site 1 (5 estates) separately by including Treatment as a factor in the first run and running two separate analyses on BMP and REF accordingly. This is done to find the most important explanatory factors for yield trends at site and estate level, and between BMP and REF treatments. A Backward elimination method for predictor selection was chosen and compared with Forward and Stepwise selection to check whether all three methods would select the same predictors. Backward and Stepwise selection gave similar results, while Forward selection always included less predictors in the model. Based on this I chose to use Backward selection, because the models had a higher accuracy and a greater variety of predictors. Partial correlations were included to the SPSS Coefficients output, so each predictor s correlation to 12MRY within the model structure was given as well as whether it was positively or negatively related to yield. Partial correlations indicate a variables unique contribution to the dependent variable and indicates to what extent the coefficient of determination (R 2 ) will decrease if that variable is removed from the regression equation [1]. It is a measure of correlation between two variables that remains after ruling out the effects of all other predictor variables in the model [24] and is therefore useful in explaining the variance in one particular variable from a set of predictor variables [9]. The magnitude of the partial correlation coefficients and significance of the predictors were chosen as criteria for explaining a factor s contribution to yield trends.

17 Yield (t ha -1 ) Yield (t ha -1 ) Results I. Analysis 1 i. Between treatments Yield with BMP was significantly (P<0.05) larger for all years (Table 11, Figure 4). BMP yield averaged 3.5 t ha -1 (+14.8%) more than the REF yield of 23.7 t ha -1. Both BMP and REF yields significantly declined from project year 2 to 3 (-0.9 t ha -1 for BMP (-3.2%) and -0.7 t ha -1 (-3.0%) for REF). BMP yield increased slightly again from year 3 to 4, although insignificant, while REF yield continued to decline. The overall decline in yield from year 2 to 4 was -0.6 t ha -1 (-2.4%) for BMP and -0.8 t ha -1 (- 3.4%) for REF (Table 11). Table 11. Average yields (ton ha -1 ) per treatment for each project year and yield differences between project years and treatment Project year Yield (t ha -1 ) 1 Difference Project year 6 Difference BMP 7 REF 8 BMP 2 REF 3 Yield 4 % 5 Yield % Yield % and and and Avg average annual yield in tonnes per hectare; 2 mean values for 30 BMP blocks (across 6 sites); 3 mean values for 30 REF blocks (across 6 sites); 4 difference between BMP and REF in ton ha-1; 5 difference between BMP and REF in percentages; - significant yield differences between treatments (P<0.05); 6 differences between project year; 7 yield differences indicated in tonnes per hectare and percentages for BMP; 8 - yield differences indicated in tonnes per hectare and percentages for REF; - significant yield differences between project year per treatment REF BMP BMP REF Project year Project year Figure 4. Average yields in BMP and REF treatments across 6 sites for each project year 17

18 Yield (t ha -1 ) ii. Between treatments within and between sites Table 12 presents BMP and REF yields achieved at six sites throughout Kalimantan and Sumatra. BMP yield was higher for each year at every site, expect for North Sumatra 1 (NS1) in year 2 with a difference of -0.4 t ha -1 (-1.3%). Average yields with BMP were higher at each site, although no significant difference was found for NS1. The largest differences were found at North Sumatra 2 (NS2), South Sumatra (SS) and Central Kalimantan (CK); +26.5%, +28.7% and +24.3% respectively. NS1, NS2, SS and WK all experienced a decline in yield from year 2 to 4, while EK and especially CK experienced an increase in yield for BMP (Figure 5). When comparing sites and treatments with each other, a lot of yield differences occur as well (Figure 5). Only no significant differences were found for BMP between East Kalimantan (EK) and NS2 for year 2 and NS1 and EK for year 3 and 4, while NS2 and West Kalimantan (WK) achieved similar yields as REF for years 3 and 4. Figures 6 & 7 show the yield-data presented on a monthly basis, calculated for both actual and rolling yields for all sites. The graphs don t all start in the same year/month, because project implementation at each site is different. In addition, these graphs also illustrate why I chose to work with rolling yields, as it smooth s out yield fluctuations and allows a clearer detection in yield trends. Table 12. Average yields (t ha -1 ) per treatment and project year for each site Site 1 Project year Avg BMP 2 REF 3 Difference BMP REF Difference BMP REF Difference BMP REF Difference Yield % Yield % Yield % Yield % NS EK NS SS WK CK NS1 = North Sumatra 1, EK = East Kalimantan, NS2 = North Sumatra 2, SS = South Sumatra, WK = West Kalimantan, CK = Central Kalimantan; 2 - mean values for 5 BMP blocks; 3 mean values for 5 REF blocks; - significant yield differences between treatments (P<0.05) a b a b BMP REF North Sumatra 1 East Kalimantan North Sumatra 2 South Sumatra West Kalimantan Central Kalimantan Site and project year Figure 5. Average yields in BMP and REF treatments for site and project year. Similar notations above the bars (1,2,3 for BMP treatments and a,b for REF treatments) indicate no significant treatment differences (P>0.05) between sites

19 Yield (t ha -1 ) Yield (t ha -1 ) Actual yields - BMP NS1 NS2 SS WK CK EK Date Actual yields - REF NS1 NS2 SS WK CK EK Date Figure 6. Average actual monthly yields (t ha -1 ) of BMP and REF treatments for all sites during project implementation 19

20 apr-07 jun-07 aug-07 okt-07 dec-07 feb-08 apr-08 jun-08 aug-08 okt-08 dec-08 feb-09 apr-09 jun-09 aug-09 okt-09 dec-09 feb-10 apr-10 jun-10 aug-10 okt-10 dec-10 feb-11 apr-11 jun-11 Yield (t ha -1 ) apr-07 jun-07 aug-07 okt-07 dec-07 feb-08 apr-08 jun-08 aug-08 okt-08 dec-08 feb-09 apr-09 jun-09 aug-09 okt-09 dec-09 feb-10 apr-10 jun-10 aug-10 okt-10 dec-10 feb-11 apr-11 jun-11 Yield (t ha -1 ) 35 Rolling yields - BMP NS1 NS2 SS WK CK EK Date 35 Rolling yields - REF NS1 NS2 SS WK CK EK Date Figure 7. Average rolling monthly yields (t ha -1 ) of BMP and REF treatments for all sites during project implementation

21 iii. Between treatments within and between estates Because there was too much data to show in tables or figures, I chose two contrasting sites and their estates to describe in this chapter. NS1 is one of the highest yielding sites, but shows a decline in yield over the years (-1.6 t ha -1 (-5.5%) from year 2 to 4 for BMP and t ha -1 (-20.5%) for REF; Figure 8.C), while CK is one of the poorest yielding sites, but shows a major increase in yields over the years (+8.1 t ha -1 (+32.8%) from year 2 to 4 for BMP and t ha -1 (+27.7%) for REF; Figure 8.D). NS1 consists of 5 estates and CK of 3. Yield differences for estates at NS1 and CK are given in Table 13 and are fairly variable. Average yield differences for NS1 vary between 0.4 t ha -1 (+1.5%) for SB and 2.4 t ha-1 (+9.6%) for GB, while Figure 8.A shows that there is not much improvement in BMP at the estate level. BMP yields decline gradually for KP, while yields at all other estates are stable or show a minor decrease. Differences in REF yields are clearer and decline at a higher rate. For 3 of 5 estates (KP, SB and SE) REF yields are higher than BMP yields in year 2. Yields achieved at estates in CK were much smaller than in NS1, but showed much stronger improvements for both BMP and REF. Average yield differences vary between 3.9 t ha -1 (+22%) for SU and 4.4 t ha -1 (+25.8%) for SS, while BMP and REF yields increase over the years for all 3 estates as well (Figure 8.B). The observed yield trends for BMP and REF at the estate level most likely explain the yield trends at the site level (Figure 8.C,D). Figure 9 shows a more detailed representation of the data by presenting yields on a monthly basis for all estates at both sites. It must be noted that palmage in estates at NS1 and CK are highly variable (Table 5) and could affect yield-differences to some extent. Table 13. Average yields (ton ha -1 ) per treatment and project year for two estates in North Sumatra 1 and Central Kalimantan Site Est. 1 Project year Avg BMP 2 REF 3 Difference BMP REF Difference BMP REF Difference BMP REF Difference Yield % Yield % Yield % Yield % NS1 GB b 26.5 c d e KP a b d e SB a b 27.5 c e SE TR CK SS SU WA 16.5 f 14.0 h g 14.5 h,i j 17.0 k j l,m 20.0 n l 20.1 n o 17.0 p o 17.7 p f,g 15.0 i j 16.8 k m 19.6 n o 17.1 p estate; 2 - mean values for 1 BMP block for NS1 and 2 (SS), 1 (SU) and 2(WA) for CK; 3 - mean values for 1 REF block for NS1 and 2 (SS), 1 (SU) and 2(WA) for CK; a-p similar notations indicate no significant differences (P>0.05) between treatments; - significant yield differences between treatments (P<0.05). 21

22 Yield (ton/ha) Yield (ton/ha) Yield (ton ha -1 ) Yield (ton ha -1 ) A North Sumatra 1 B Central Kalimantan BMP REF BMP REF GB KP SB SE TR Estate and project year SS SU WA Estate and project year C D Project year BMP REF Project year BMP REF Figure 8. A - average yields in BMP and REF treatments for estates in North Sumatra 1 for each project year; B - average yields in BMP and REF treatments for estates in Central Kalimantan for each project year; C average yields in BMP and REF treatments for North Sumatra 1 and project year; D - average yields in BMP and REF treatments for Central Kalimantan and project year; - significant yield differences between project year for BMP treatment; - significant yield differences between project year for REF treatment

23 Yield (t ha -1 ) Yield (t ha -1 ) A North Sumatra BMP GB BMP KP BMP SB BMP SE BMP TR REF GB REF KP REF SB REF SE REF TR Date B Central Kalimantan BMP SS BMP SU BMP WA REF SS REF SU REF WA Date Figure 9. Average monthly rolling yields (t ha -1 ) of BMP and REF treatments for estates at North Sumatra 1 (A) and Central Kalimantan (B) during project implementation 23

24 Yield (ton ha -1 ) iv. Between treatments within and between climatic zones Table 14 shows BMP and REF yields achieved in 4 different climatic zones; Extremely wet (EW), Wet (W), Slightly wet (SW) and Slightly dry (SD). Average yield differences were greatest in W and EW zones; +4.7 (+27.1%) t ha -1 and +3.7 t ha -1 (+15.6%) respectively, while SW and SD zones gave higher yields for both BMP and REF (Figure 10). All climatic zones showed a decline in yield from year 2 to 4 for BMP, except for W which increased with +2.2 t ha -1 (+2.2%). Between climatic zones and treatments, no differences were found between SW and SD for year 3 and 4 for BMP and REF (Figure 10). All other climatic zones and years gave significantly different yields. Table 14. Average yields (ton ha -1 ) per treatment and project year for each climatic zone Climatic Project year zone Avg BMP 2 REF 3 Difference BMP REF Difference BMP REF Difference BMP REF Difference Yield % Yield % Yield % Yield % EW W SW SD EW = Extremely wet, W = Wet, SW = Slightly wet, SD = Slightly dry; 2 - mean values for 15 BMP blocks for EW, 10 for W, 3 for SW and 2 for SD; 3 mean values for 15 REF blocks for EW, 10 for W, 3 for SW and 2 for SD; - significant yield differences between treatments (P<0.05) a a b b BMP REF Extremely wet Wet Slightly wet Slightly dry Climatic zone and project year Figure 10. Average yields in BMP and REF treatments for climatic zone and project year. Similar notations above the bars (1,2 for BMP treatments and a,b for REF treatments) indicate no significant treatment differences (P>0.05) between climatic zones

25 v. Between treatments within and between soil textures While not all soil textures occurred on both BMP and REF blocks (e.g. Coarse sand (CrS) only on REF blocks and Fine sand (FiS) only on BMP blocks), average yields with BMP were larger on each soil texture (Table 15), ranging from +1.6 t ha -1 (+6.8%) for Fine sandy loam (FiSL) to +8.3 t ha -1 (+48.2%) for Sandy clay (SC). Table 16 shows in which years significant differences between different types of soil textures occur. For example, Clay (C) soils are significantly higher yielding than Clay Loam (CL) soils in project year 2, while in project year 4, CL soils are significantly higher yielding than C soils for BMP treatments. Table 15. Average yields (ton ha -1 ) per treatment and project year for each soil texture Soil texture 1 Project year Avg BMP 2 REF 3 Difference BMP REF Difference BMP REF Difference BMP REF Difference Yield % Yield % Yield % Yield % C CL CrS CrSL FiS FiSL LCrS LFiS SC SCL C = Clay, CL = Clay loam, CrS = Coarse sand, CrSL = Coarse sandy loam, FiS = Fine sand, FiSL = Fine sandy loam, LCrS = Loamy coarse sand, LFiS = Loamy fine sand, SC = Sandy clay, SCL = Sandy clay loam; 2 - mean values for 3 BMP blocks for C, 3 for CL, 3 for CrSL, 3 for FiSL, 6 for LCrS, 1 for LFiS, 4 for SC and 7 for SCL; 3 mean values for 4 REF blocks for C, 6 for CL, 1 for CrS, 4 for CrSL, 1 for FiS, 4 for FiSL, 3 for LCrS, 1 for SC and 6 for SCL; - significant yield differences between treatments (P<0.05). Table 16. Significant yield differences between soil textures presented by project year Treatment Soil CL CrS CrSL FiS FiSL LCrS LFiS SC SCL texture BMP C 2,4 2,4 2 2,3,4 2,3 2,3 2,4 CL 3 2,4 2,3,4 4 2,4 2 CrSL 2,3,4 2,3,4 3,4 2,3,4 2 FiSL 2,3 2 2,3 3,4 LCrS 2 2 2,3,4 LFiS 2 2,3,4 SC 3,4 REF C 2,3,4 2 2,3,4 2,3 2,3,4 2 2,3,4 3,4 CL 3,4 2,4 2,3 2,3,4 2,3,4 2,3 CrS 2,3,4 3 2,3,4 2 2,3,4 CrSL 2,4 3 2,3,4 2,3,4 2,3 FiS 2,3, ,3,4 FiSL 2,3,4 2,3,4 2 LCrS 4 2,3,4 SC 2,3,4 2,3,4 - project year. When indicated within a cell, it means that there is a significant difference in yield (P<0.05) between the two corresponding soil textures in that particular year. 25