Original papersA new decision-support system for the historical analysis of integrated pest management activities on olive crops based on climatic data
Introduction
Olive tree (Olea europea L.) is one of the most important crop at global scale. The cultivated surface at world level is equal to 10.27 Mha with a production of 15.4 Mt (FAOSTAT, 2014). The leading three countries for Olive crop invested area are Spain (2.52 Mha), Tunisia (1.59 Mha) and Italy (1.15 Mha), while at production level there are Spain (4.56 Mt), Italy (1.96 Mt) and Greece (1.78 Mt) (FAOSTAT, 2014). According to the Italian Institute of Statistics (ISTAT, 2016), Apulia is the first olive-producing region in Italy. In particular, in 2016 the agricultural surface invested in Apulia for olive production was equal to 0.379 Mha, thus representing 33% of the corresponding national rate (1.14 Mha). The obtained production was 0.99 Mt, corresponding to 35% of the national amount (2.81 Mt) (ISTAT, 2016).
Therefore, in the Apulia region, a large number (227245) of farms works on the Olive crop (ISTAT, 2010), which generates a huge amount of Integrated Pest Management (IPM) data. Actually, these data are quite unexploited. The IPM approach consists of the application of different crop’s protection methods in order to maintain the pest populations below an economic damage threshold (Chandler et al., 2011). Therefore, the IPM crop protection strategies are composed of different techniques to control insects, pathogens, weeds and vertebrates, with an ecological and economic approach (Prokopy, 2003). With the Framework Directive 2009/128/EC (European Parliament, 2009) on Sustainable Use of Pesticides, the European Union (EU) has highlighted that the application of the IPM approach could be the answer to decrease the reliance on the use of conventional pesticides. Therefore, since the 1st January 2014 in the European member countries is mandatory for all the professional users of pesticides to implement the IPM strategies. The Framework Directive 2009/128/EC requires that each Member States must release the crop technical specifications, combining the eight IPM principles (Barzman et al., 2015).
Nowadays with the democratization of information technologies, agriculture enquiries more and more the usage of these technologies in all its practices. Therefore, Farm Management Information Systems (FMISs) have been developed. FMISs are systems able to carry out functionalities for field operations management, best practice tools, finance, inventory, traceability, reporting, site-specific tools, sales, machinery management, human resource management, and quality assurance (Fountas et al., 2015). In many commercial FMISs, the current functions related to best practices, such as adherence to organic standards or IPM requirements, are still in their infancy (Fountas et al., 2015).
Taking into account the environmental (i.e. climatic) factors is a mandatory task to create a real useful instrument to help farmers to analyze the management choices. In fact, the environmental variables, such as air temperature, relative humidity, solar radiation, rain, etc., interacting constantly with the plants and the pests, as reported by many authors in literature. In particular, radiation, temperature, and water have a great impact on the growth of plants (Hay and Porter, 2006).
As highlighted in Zaza et al. (2018), historical analysis of IPM data is necessary to understand good practices, and discover trends and farming behaviors. Therefore, in order to define an efficient tool for the historical analysis of IPM farm data, Zaza et al. (2018) proposed the usage of a dedicated Data Warehouse (DW) and the On-Line Analytical Processing (OLAP) system, as a decision-making tool of FMISs. In particular, in Zaza et al. (2018), the authors have provided a Business Intelligence tool, called BI4IPM, which could be conveniently used to verify the compliance of the farm operations with the requirements included in the olive crop IPM technical specification provided by the Apulia region. By means of BI4IPM, decision-makers can answer to queries like this: “What are sustainable practices of each farm of Apulia region during 2017?”.
However, this work suffers of an important limitation: the OLAP model does not take into account climatic data. In this way it is difficult to compare IPM data over different campaigns and farms (which can be geographically distributed over a large area), since time and space can be associated to different climate, and therefore with important impacts on cultures, as above described. For example, it is not possible to answer decisional queries like this: “What are sustainable practices of each farm of Apulia region during 2017 that had the same climatic conditions?”
Thus, considering the mandatory and very important relation between climate data on crops and pests, in this paper we extend our previous work (Zaza et al., 2018) integrating climatic data into OLAP model. In particular, we use the Growing Season Index (GSI), a phenology model, to indirectly compare the farms by a climatic point of view. Indeed, phenology is used as an indicator to evaluate the effects of climate change in short and long periods, because it is driven by environmental variables, such as temperature, precipitation and photoperiod (Xu et al., 2014). The GSI is a model developed for the prediction of plant phenology in response to low temperatures, evaporative demands and photoperiod, applicable at global scale (Jolly et al., 2005). In this work, we use a modified version of the GSI proposed by Orlandi et al. (2013). The main difference is that the “original” GSI was developed and tested for the assessment of canopy foliar dynamics on different vegetal species at global scale, while the second version is adapted to the olive trees and tested in several locations in the Mediterranean basin. The integration of the GSI data into the OLAP model of Zaza et al. (2018) has been achieved by a new advanced OLAP model, which has obtained using the adaptation of a new DW design methodology proposed by Sautot et al. (2015). Indeed in our application, the usage of existing DW design methodologies outputs an OLAP model that is not enough expressive to allow decision-makers to query IPM data and correlate them to climatic data.
Moreover, since GSI data is usually visualized using graphical data, in this paper we present a new OLAP client (i.e. the OLAP visualization tool) that is able to represent OLAP data by means of pivot tables for numerical data and also for graphic displays. This new OLAP client allows the simple at glance visual analysis of IPM and GSI data.
We have validated our approach using some real data concerning farms of the Bari department, and open climatic data provided by Apulia Regional Agency for the Prevention and Protection of the Environment (ARPA-Puglia, 2017).
The paper is organized as follow: Section 2 presents a brief background on DW and OLAP approach, IPM context at Apulia Region scale and GSI model; Section 3 is dedicated to the definition of the Apulian case study, exploring the climatic and farming data adopted; Section 4 details the OLAP model for agro-phenological data; Section 5 presents a set of OLAP advanced analysis achieved; Section 6 provides an overview regarding the implementation of the OLAP system; Section 7 explores the related works regarding the phenology models and OLAP for agricultural data; Section 8 concludes the paper and provides the future perspective.
Section snippets
Background
In this section, we present main concepts of technologies and methods adopted in this work: Section 2.1 presents Data Warehouse and OLAP systems, Section 2.2 describes an overview of Integrated Pest Management for olive trees, and Section 2.3 presents details of GSI model.
Case study
In this section we introduce the data we have used to validate our proposal. In particular, Section 3.1 presents climatic data, and Section 3.2 describes farming data.
OLAP model for agro-phenological data
As discussed in Section 2.1, mixed DW design methodologies take into input data sources and user needs and they output an OLAP model, and are the most used for real-life OLAP applications.
Then, in this section, we present two OLAP models for the integration of GSI and IPM data.
The generic framework used to achieve this integration is described in Fig. 2. The main idea is to use data sources containing farming and climatic data. Then, climatic data are used to obtain phenological data using the
OLAP advanced analyses: results
In this section we present a set of examples that are representative of the analysis possibilities offered by our new OLAP model.
Among all analysis capabilities offered by the model (c.f. (Zaza et al., 2018) for more details), in this work, in particular, we show how it is possible to analyze IPM data of different farms having the same phenological conditions over a year to understand some best practices (Section 5.1).
Moreover, we present some examples of the analysis of the practices, and
Implementation
The OLAP system, based on the advanced model described in Fig. 6, is a three-tier relational OLAP system structured as follows (Fig. 13):
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The Data Warehouse tier. This level is in charge for data storage and it is carried out using the Postgres DBMS. Data are modeled using the star schema logical model (Kimball and Ross, 2013). Star schema denormalizes dimension tables to avoid expensive join operations.
The star schema model implemented in our case study is shown in Fig. 14.
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The OLAP Server tier.
Related work
The vegetation phenology studies the recurrence of vegetative cycles in function of climate conditions. Robust phenology models have been used to monitor and predict the vegetation response to climatic data (White et al., 1997). In literature, many works are focused on phenology as an indicator to evaluate the effects of climate change (Kramer, 1996, Lechowicz and Koike, 1995, Schwartz, 1999), because it is driven by environmental variables, such as temperature, precipitation and photoperiod (
Conclusion and future work
Olive tree is one of the most important crop at global scale. Apulia is the first region for olive crop in Italy, producing 0.99 Mt, equal to 35% of the national amount (2.81 Mt) (ISTAT, 2016). Therefore, in the Apulia region, a large number (227245) of farms work on the Olive tree (ISTAT, 2010), generating a huge amount of IPM data.
In this paper we present an extended version of the OLAP model proposed in our previous work (Zaza et al., 2018), integrating the olive GSI, a phenology model
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