TY - JOUR
T1 - Using agrometeorological data to assist irrigation management in oil palm crops
T2 - A decision support method and results from crop model simulation
AU - Culman, María
AU - de Farias, Claudio M.
AU - Bayona, Cristihian
AU - Cabrera Cruz, José Daniel
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - In order to achieve optimum yields in oil palm, management practices should be tailored to the crop site agro-ecological conditions. Nevertheless, oil palm farmers often have to make decisions based on a limited knowledge base. Considering that water management is a critical aspect of oil palm crops, this paper describes an inference method for irrigation decision-making in oil palm supported on soil moisture and vapor pressure deficit data. Under an ideal scenario where this agrometeorological data is available through a Wireless Sensor Network (WSN) at a crop plot resolution, we formulated a method to prevent oil palm farmers to submit their crops to water deficit stress. The inference method was based on a Data Fusion technique called Dempster-Shafer Inference, which is convenient for the use of uncertain data with distinct levels of detail such as those present in a WSN. The outcome of fusing soil moisture and vapor pressure data was inferring the crop state, regarding soil and plant water status, following the concept of Site-specific Agriculture. To evaluate the impact of the method on crop yield, we carried out two simulations. The first one on a WSNs simulator, Castalia, to generate the irrigation decisions according to the site-specific agrometeorological data collected from the WSN. The second one on a crop simulation model, APSIM (Agricultural Production Systems Simulator), to simulate the oil palm plot at the study site under two treatments: plot with irrigation managed by the inference method and plot without irrigation. Results from oil palm crop simulation showed a 27% increase in the production of bunches of fresh fruit between 2016 and 2017 in the treatment with irrigation. The method has the potential for contributing to irrigation decision-support systems and for being useful in yield-intensification rather than crop-extension politics for oil palm and other crops.
AB - In order to achieve optimum yields in oil palm, management practices should be tailored to the crop site agro-ecological conditions. Nevertheless, oil palm farmers often have to make decisions based on a limited knowledge base. Considering that water management is a critical aspect of oil palm crops, this paper describes an inference method for irrigation decision-making in oil palm supported on soil moisture and vapor pressure deficit data. Under an ideal scenario where this agrometeorological data is available through a Wireless Sensor Network (WSN) at a crop plot resolution, we formulated a method to prevent oil palm farmers to submit their crops to water deficit stress. The inference method was based on a Data Fusion technique called Dempster-Shafer Inference, which is convenient for the use of uncertain data with distinct levels of detail such as those present in a WSN. The outcome of fusing soil moisture and vapor pressure data was inferring the crop state, regarding soil and plant water status, following the concept of Site-specific Agriculture. To evaluate the impact of the method on crop yield, we carried out two simulations. The first one on a WSNs simulator, Castalia, to generate the irrigation decisions according to the site-specific agrometeorological data collected from the WSN. The second one on a crop simulation model, APSIM (Agricultural Production Systems Simulator), to simulate the oil palm plot at the study site under two treatments: plot with irrigation managed by the inference method and plot without irrigation. Results from oil palm crop simulation showed a 27% increase in the production of bunches of fresh fruit between 2016 and 2017 in the treatment with irrigation. The method has the potential for contributing to irrigation decision-support systems and for being useful in yield-intensification rather than crop-extension politics for oil palm and other crops.
KW - Data fusion
KW - Decision making
KW - Irrigation management
KW - Oil palm
KW - Site-specific agriculture
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85059313546&partnerID=8YFLogxK
U2 - 10.1016/j.agwat.2018.09.052
DO - 10.1016/j.agwat.2018.09.052
M3 - Artículo Científico
AN - SCOPUS:85059313546
SN - 0378-3774
VL - 213
SP - 1047
EP - 1062
JO - Agricultural Water Management
JF - Agricultural Water Management
ER -
