Original papers
A new decision-support system for the historical analysis of integrated pest management activities on olive crops based on climatic data

https://doi.org/10.1016/j.compag.2018.03.015Get rights and content

Highlights

  • New OLAP system integrating the GSI model.

  • Historical farming practises analysis in different or similar phenological conditions.

  • Apulian case study with real farming and climatic data.

  • New OLAP client able to manage pivot tables for numerical data and also for graphic displays.

Abstract

Olive tree is one of the most important crop at global scale. Apulia is the first olive-producing region in Italy, with a huge amount of farms that generate Integrated Pest Management (IPM) data. IPM requires the simultaneous use of different crop protection techniques to control pests through an ecological and economic approach. The crop protection strategies are correlated to the climate condition considering the very important relation among climate, crops and pests. Therefore, in this work is presented a new advanced On-Line Analytical Processing (OLAP) model integrating the Growing Season Index (GSI), a phenology model, to compare indirectly the farms by a climatic point of view. The proposed system allows analysing IPM data of different farms having the same phenological conditions over a year to understand some best practices and to highlight and explain different practices adopted by farms working in different climatic conditions.

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):

  • 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.

  • 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

References (72)

  • M. Barzman et al.

    Eight principles of integrated pest management

    Agron. Sustainable Dev.

    (2015)
  • Bimonte, S., Boulil, K., Pinet, F., Kang, M.A., 2013. Design of complex spatio-multidimensional models with the ICSOLAP...
  • Bimonte, S., Chanet, J.P., Capdeville, J., Lefrileux, Y., 2014. Energetic assessment of dairy activities using OLAP...
  • S. Bimonte et al.

    A New sensor-based spatial OLAP architecture centered on an agricultural farm energy-use diagnosis tool

    Int. J. Decision Support Syst. Technol. (IJDSST)

    (2013)
  • R. Borchert et al.

    Photoperiodic control of seasonal development and dormancy in tropical stem-succulent trees

    Tree Physiol.

    (2001)
  • H.J. Burrack et al.

    Olive fruit fly (Diptera: Tephritidae) ovipositional preference and larval performance in several commercially important olive varieties in California

    J. Econ. Entomol.

    (2008)
  • A. Caffarra et al.

    Increasing the robustness of phenological models for Vitis vinifera cv. Chardonnay

    Int. J. Biometeorol.

    (2010)
  • D. Chandler et al.

    The development, regulation and use of biopesticides for integrated pest management

    Philos. Trans. Royal Soc. London B: Biol. Sci.

    (2011)
  • K. Chaturvedi et al.

    On-line analytical processing in agriculture using multidimensional cubes

    J. Ind. Soc. Agril. Statist

    (2008)
  • Chaudhary, S., Sorathia, V., Laliwala, Z., 2004. Architecture of sensor based agricultural information system for...
  • S.L. Childes

    Phenology of nine common woody species in semi-arid, deciduous Kalahari Sand vegetation

    Vegetatio

    (1988)
  • A. Crovetti et al.

    Influence of temperature and humidity on the development of the immature stages of Dacus oleae (Gmelin)

    Frustula Entomol.

    (1982)
  • R. Deggau et al.

    Interacting with spatial data warehouses through semantic descriptions

    GeoInfo

    (2010)
  • Donatelli, M., Campbell, G.S., 1998. A simple model to estimate global solar radiation. In: Proceedings of the Fifth...
  • W.E. Easterling et al.

    Food, fibre and forest products

    Clim. Change

    (2007)
  • European. Parliament

    Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for Community action to achieve the sustainable use of pesticides

    Off. J. Eur. Union

    (2009)
  • FAOSTAT (2014) available at http://www.fao.org/faostat/en/#data/QC (accessed on...
  • B.S. Fletcher et al.

    Changes in the ovaries of olive flies (Dacus oleae (Gmelin)) during the summer, and their relationship to temperature, humidity and fruit availability

    Ecol. Entomol.

    (1978)
  • C. Gallo et al.

    Data warehouse design and management: theory and practice

    IEEE Members. Dipartimento di Scienze Economiche, Matematiche e Statistiche Università Di Foggia Largo Papa Giovanni Paolo

    (2010)
  • García de Cortázar-Atauri, I., Brisson, N., Seguin, B., Gaudillere, J.P., Baculat, B., 2005. Simulation of budbreak...
  • H. Genç et al.

    Survival and development of Bactrocera oleae Gmelin (Diptera: Tephritidae) immature stages at four temperatures in the laboratory

    Afr. J. Biotechnol.

    (2008)
  • C. Granier et al.

    Water deficit and spatial pattern of leaf development. Variability in responses can be simulated using a simple model of leaf development

    Plant Physiol.

    (1999)
  • Gregory, P.J., Ingram, J.S., Campbell, B., Goudriaan, J., Hunt, L.A., Landsberg, J.J., Linder, S., Stafford Smith, M.,...
  • A.K. Gupta et al.

    Multidimensional schema for agricultural data warehouse

    Int. J. Res. Eng. Technol.

    (2013)
  • R. Häkkinen et al.

    Effects of dormancy and environmental factors on timing of bud burst in Betula pendula

    Tree Physiol.

    (1998)
  • R.K.M. Hay et al.

    The Physiology of Crop Yield

    (2006)
  • Cited by (13)

    • The effect of long-term climatic variability on wild mammal populations in a tropical forest hotspot: A business intelligence framework

      2023, Ecological Informatics
      Citation Excerpt :

      Business Intelligence systems are flexible exploratory tools which give support to the discovery, integration, and analysis of massive volumes of heterogeneous data (Ain et al., 2019). For several years, its application was restricted to the improvement of managerial practices of companies, but, most recently, it has been applied to a range of scientific fields (Bimonte et al., 2021; Zaza et al., 2018a, 2018b). In Ecology, the adoption of BI systems is still in the early stages and has been mainly directed towards environmental monitoring (Villar et al., 2018; Zaza et al., 2018b).

    • The pesticide fate tool for groundwater vulnerability assessment within the geospatial decision support system LandSupport

      2022, Science of the Total Environment
      Citation Excerpt :

      In such complex and difficult framework, it is self-evident that the availability of freely available operational systems, enabling to connect and manage agriculture in view of water quality, is vital and it may support a better implementation of the above-mentioned water protection directives. In the last decade, Decision Support Systems (DSS) turn out to be very powerful instruments in the hands of planners/decision-makers, allowing - among many other features - the on-the-fly modelling, supporting the what-if scenarios (Yalew et al., 2016; Lindblom et al., 2017; Zaza et al., 2018; Marano et al., 2019; Manna et al., 2020; Nicholson et al., 2020). Between their several applications, DSS can be used, for example, to explicitly simulate the flow of water and the transport of dissolved contaminants, through the saturated and unsaturated zones, at different spatio-temporal scales (Terribile et al., 2015).

    • Managing complex datasets to predict Bactrocera oleae infestation at the regional scale

      2020, Computers and Electronics in Agriculture
      Citation Excerpt :

      Furthermore, ML models can be used in decision support systems to deliver early-warning advice, tailored to changing environmental conditions (Zhai et al., 2020). In general, IPM of B. oleae is based on monitoring activity to assess pest infestation on fruits in olive tree groves, in order to determine whether the intervention threshold is reached (e.g. Zaza et al., 2018). However, in sustainable olive tree growing, the application of preventive strategies is imperative to direct high-quality production and environment safeguard.

    • A geospatial decision support system to assist olive growing at the landscape scale

      2020, Computers and Electronics in Agriculture
      Citation Excerpt :

      In recent years, the scientific community has made many contributions to better olive grove management and planning by applying/developing models, mapping procedures and also DSSs (Decision Support Systems). Among those: (i) field scale new management and sustainable approaches (La Scalia et al., 2016; Russo et al., 2015; Caruso et al., 2014), (ii) pest management by webGIS application, DSS and olive fly modelling (Zaza et al., 2018; Doitsidis et al., 2017; Pontikakos et al., 2012). Many of these approaches are based on modelling; models applied to olive grove are typically classified as either empirical/statistical or dynamic.

    • The LandSupport platform to help land managers in the mitigation of degradation of natural resources

      2023, 2023 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2023 - Proceedings
    View all citing articles on Scopus
    View full text