Cocoa and coffee agroforestry on hillslopes: root, soil structure, infiltration and ecological restoration

By danny.dwi.saputra , 12 May, 2022
Organizational Context
Name
Danny Dwi Saputra
Chairgroup
Plant Production System (PPS)
Graduate school
Production Ecology and Resource Conservation (PE&RC)
Start date of project
Abstract
Together with Ivory Coast and Ghana, Indonesia accounted for two-thirds of cocoa bean production in 2014. It is also one of the top-10 coffee producers of the world. However, cocoa and coffee productivity, quality and consistency are deteriorating. The differences of plant and soil management in agriculture systems lead to differences in soil degradation rate and nutrient losses, which ultimately influence soil fertility and crop productivity. On the other hand, as a country with a hundred of active volcanoes, Indonesia is actually gifted with fertile volcanic soils that are well suited for plant growth. However, the deposition of ash during a volcanic eruption in the short term has a detrimental effect on soil's physical properties as well as ecological functions. Nevertheless, soil can develop as a self-regenerating system against degradative processes known as ‘the internal restoration’, mainly through the adoption of restorative management systems such as agroforestry. However, there is only limited empirical understanding of best management options for agroforestry practices as well as it shapes resiliency toward soil degradation and extreme condition. The objectives of this research are: a. understanding the role of soil organic matter and root density in the restoration of soil aggregate stability, macroporosity and infiltration, b. exploring best management options in cocoa-based agroforestry, c. exploring the resilience of coffee-based agroforestry compared to natural forest and cropped fields towards extreme stress conditions (volcanic ash deposition), d. exploring best management options in coffee-based agroforestry system related to plant yield and surface hydrological function using WaNuLCAS model simulation.

 

Role supervisor

Daily supervision will be done by the supervisors Prof. Kurniatun Hairiah and Prof. Didik Suprayogo. Prof. Meine van Noordwijk is the overall supervisor and promotor of the PhD student. At least one of the supervisory member will meet (online or offline) with the PhD student every week.

Who's collecting the data

PhD candidate responsible for collecting and managing data, with the help of colleagues and students. For some plot data collections, we collaborated with PhD candidate Rika Ratna Sari (Plant Production System groups) as we share the research site and parts of our research were funded within the same research project. Parts of this research were funded through AgFor Project (World Agroforestry Centre/ICRAF SE Asia), and other parts were funded by Brawijaya University through PUPT, HPP and BOPTN scheme, The Ministry of Higher Education (DIKTI) through the HIRD scheme.

Who's analysing the data

The PhD candidate will analyse the data with feedback from supervisors and colleagues

Location short term storage

All data will be stored on my local harddisk in a folder called Thesis.

Within this Thesis folder, I'll create per chapter the folders: Data, Model, Paper and Scripts. The Data folder has two sub-folders called: Raw and Processed.

Folder contents:

  • Data - Raw sub-folder: Contains all raw data and meta-data (a description of your data).
  • Data - Processed sub-folder: Contains all processed data. 
  • Model folder: Complete listing of the model and the model results & analysis.
  • Paper folder: Text of a chapter / paper.  
  • Scripts folder: Contains all scripts used.
Backup procedure

The complete content of my local Thesis folder will be stored on my YoDa-drive. 

During periods I'm abroad, I'll backup the complete content of my local Thesis folder to a Dropbox or MS-Onedrive Thesis folder and share the contents with my supervisor(s). 

Research data with value for long term storage

Datasets, publications, posters and presentations generated, as well as scripts might be stored for long term storage.

Research data excluded for long term storage. Why?

Data collected by different research organisations and used in my research may be excluded from long term storage. Clear agreements regarding (long term) storage will be made with the owners of the data under confidentiality agreements if needed.

Plans for sharing data?

For all data and outputs, there is the intention to make these publicly available, in line with the policies if the research organisation funding it.

How to access data once you leave?

Data will be stored in the Yo-Da drive and will also share with all the partners of the project as final (cleaned) data. If data cannot be published with open access there is an option to restrict access to the data. In those cases, there will be a contact person to request access to the data.

Other parties involved? Agreements on data sharing?

Data and information collected within the research project are often a joint responsibility of all partner organizations (WUR, ICRAF, Brawijaya University).

Other persons contributing (e.g. writing code)

Colleagues (Rika Ratna Sari) who are involved in some parts of the research project.

Privacy, security issues? How you deal with them?

All privacy-sensitive data will be excluded from the datasets prior to storing/sharing/publishing