Plantation Intelligence

Transcription

1 Plantation Intelligence Analysis of Commercial Data for Yield and Fertilizer Management in Oil Palm Oberthür, Chua, Cook, Donough, Cock, Lim, Mohanaraj, Rachel & Kam XVIII Conferencia Internacional Sobre Palma de Aceite 18 th International Oil Palm Conference 22 al 25 de Septiembre de 2015 Cartagena de las Indias, Colombia

2 Conceptual Background

3 IPNI 4R Nutrient Stewardship

4 Decision Making Uncertainty Complex interactions in an agronomic system render outcomes of any management decision Uncertain

5 Decision Making Uncertainty METRIC Uncertainty about rate & placement of fertilizer to support a yield target Example: ROI in Fertilizer TRANSLATIONAL Uncertainty from external factors that reduce fertilizer performance, e.g. harvest, mill and transport efficiency TEMPORAL Uncertainty about timing of fertilizer applications, e.g. drought interference STRUCTURAL Uncertainty from internal factors that influence fertilizer efficiency, e.g. EFB applications Cook et al. Better Crops 97, (2013)

6 Business Intelligence Hans Peter Luhn, IBM Journal, 1958 A Business Intelligence System (business) intelligence is the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.

7 Business Intelligence

8 Plantation Intelligence System is Monitored in Extraordinary Detail

9 Plantation Intelligence Exogenous Organize existing performance data (VISUALIZE) Quantify performance change for management (DECIDE) Generate performance indicators and metrics (ANALYSE) Devise specific performance intervention options (EVALUATE) Exogenous

10 Plantation Intelligence An adaptive learning process based on the analysis of plantation data

11 Application Examples

12 Current Protocols Yield Age Profiling Yield Soil Interactions Yield Trends Yield Soil Climate Interactions Fertilizer Response Analyses Naïve Gross Margins Yield Labor Interactions

13 Yield Age Profiling FFB (t/ha) TREEAGE

14 Yield Age Profiling FFB (t/ha) TREEAGE

15 Yield Benchmarking FFB (t/ha) Average yield profile mirrors that of potential 3. Blocks approach yield potential 4. Blocks above potential require further assessment Yield gap between average and potential PALMSIM Model Agricultural Systems, 131:1-10 (2014) The Planter, 91: (2015) TREEAGE

16 Naïve Gross Margins 5500 Cost = 500 $US per ha per year i.e. a favorable benchmark Average naïve gross margin in USD per hectare Individual Block

17 Naïve Gross Margins 5500 Average naïve gross margin in USD per hectare Individual Block

18 Naïve Gross Margins 5500 Average naïve gross margin in USD per hectare Individual Block

19 Response to Fertilizer

20 Return on Investment NPKMg BREAK EVEN 8~10 kg kg point fresh fruit bunches per kg nutrients

21 Return on Investment NPKMg Sum of NPKMg 2.5 years to 0.5 year before harvest

22 Tree Age Effects Steep Ascent : Palm age from 3-5 Plateau : Palm age from Fresh fruit bunches in tons per hectare y= (0.011*x) Fresh fruit bunches in tons per hectare y= (0.005*x) NPKMg3yW_IN NPKMg3yW_IN

23 Annual Response Fresh fruit bunches in tons per hectare NPKMg3yW_IN

24 Annual Response y= (0.024*x) y= (-0.015*x) y= (-0.022*x) y= (-0.003*x) SMG A SMG B SMG C SMG D Fresh fruit bunches in tons per hectare NPKMg3yW_IN

25 Annual Response Fresh fruit bunches in tons per hectare NPKMg3yW_IN

26 Annual Response y= (-0.054*x) y= (0.025*x) y= (0.025*x) y= (0.036*x) SMG A SMG B SMG C SMG D 28 Fresh fruit bunches in tons per hectare NPKMg3yW_IN

27 Visualizing Local Response Contrast Method Yield Response (t FFB ha y) = F 1 (PV 1, PV 2, PV N, PV NPKMg ) Yield Response (t FFB ha y) = F 2 (PV 1, PV 2, PV N ) Response Contrast (t FFB ha y) = F 2 F 1

28 Visualizing Local Response Contrast in t FFB per ha per year relative to yield without fertilizer

29 Visualizing Local Response Contrast in t FFB per ha per year relative to yield without fertilizer

30 Final Remarks

31 Decision Making Uncertainty Fresh Fruit Bunch Yield in t per ha Harvest Man days

32 Decision Making Uncertainty Fresh Fruit Bunch Yield in t per ha Expected yield level Harvest Man days

33 Decision Making Uncertainty Fresh Fruit Bunch Yield in t per ha Fruit is grown but not harvested Only with enough labor, all fruit is harvested Expected yield level Harvest Man days

34 Decision Making Uncertainty EXPECTATION Yield Made VS. Yield Taken REALITY

35 Systems Solutions Business Opportunity PALMSIM Model Measure Performance Plantation Intelligence Estate Networking The Benchmark Club Continuous Improvement Best Management Practices

36 IPNI Southeast Asia Program XVIII Conferencia Internacional Sobre PALMA DE ACEITE 18th International Oil Palm Conference 4WA 22 al 25 de Septiembre de 2015 Cartagena de las Indias, Colombia