Citrus advisory system: A web-based postbloom fruit drop disease alert system

https://doi.org/10.1016/j.compag.2020.105781Get rights and content

Highlights

  • Decision support system to monitor and predict the risk of postbloom fruit drop on citrus.

  • Reducing the fungicide applications following the recommendations of the system.

  • Using leaf wetness and temperature to predict disease outbreaks on citrus.

Abstract

Postbloom fruit drop (PFD) is a severe fungal disease of citrus that causes fruitlets to fall off trees prematurely and is linked to wet weather during bloom. Florida growers have recently struggled with PFD outbreaks, especially during the spring of 2015. The PFD control is usually done with calendar-based applications that may not be required if environmental conditions do not promote the development of PFD. The objective of this study was to develop a web-based tool to assist citrus growers with spray decisions for managing PFD risk in Florida. Information technologies such as databases, queries, and programming languages have been used to develop this tool. The system collects weather data from the Florida Automated Weather Network (FAWN) and weather stations installed by the AgroClimate research group, and uses weather observations to run a PFD disease model and estimates the environmental favorability for infection. The system sends notifications to farmers according to the risk and recommends fungicides to be applied based on the specific PFD risk and flowering stage. The tool developed under this research is available to Florida growers under the University of Florida AgroClimate information and decision support system.

Introduction

The citrus industry faces increasingly complex challenges involving dwindling water resources in many production regions, multiple pest and disease pressures, encroachment of urban development, rising costs of inputs, labor needs, and pressures to reduce off-target environmental impact (National Research Council, 2010). Nevertheless citrus production was ranked first in the world among fruits in 2017, and it accounted for a production of 146 million tons (Chen et al., 2019). The USA produced 7.94 million tons of citrus in the 2018/2019 season with most of it produced in California (51%) and Florida (44%), while Texas and Arizona contributed with the remaining 5% (Fried and Hudson, 2020).

The Florida citrus industry has recently faced a significant number of shocks and stresses due to hurricanes, canker eradication, urban development, economic downturn, and most recently huanglongbing (HLB) (Gottwald, 2010). While HLB remains a major challenge for the industry (Gottwald, 2010), postbloom fruit drop (PFD) is also an important limiting factor in citrus cultivated in tropical and subtropical regions of the world (Gama et al., 2019). PFD is caused by the fungus Colletotrichum acutatum J. H. Simmonds, recently described as Colletotrichum abscissum Pinho & O.L. Pereira, which usually survives in groves on weeds (Frare et al., 2016), and on citrus leaves (Agostini and Timmer, 1994). The fungus is able to infect petals when they are elongated but not yet open, however, the open petals are the most susceptible to infection. PFD incidence is driven by weather conditions such as leaf wetness and air temperature. The optimal air temperature range for C. abscissum development is between 24 °C and 27 °C, however, this pathogen can grow well at air temperatures between 10 °C and 30 °C (Lima et al., 2011). Typical PFD symptoms occur on flowers and are characterized by initially peach to brown colored lesions on the petals that eventually become dark-brown and necrotic. The disease can affect a portion of the petals or the entire flower (Peres et al., 2004).

Appropriately-timed fungicide applications can provide adequate control and reduce the losses of this disease (Peres et al., 2002). The management of plant diseases is usually performed by combining different strategies that might differ in efficacy, duration of effectiveness, and cost (Shtienberg, 2013). When growers must decide about a strategy to control pest and diseases in their groves, they generally choose between spraying on a calendar basis, never spray, or spray according to recommendations (Fabre et al., 2007). Spray applications based on recommendations are usually based on empirical models that combine a risk indicator and disease thresholds, with the risk indicator representing a measurement of the risk that can decrease crop yield (Fabre et al., 2007). A decision-support system (DSS) linked with empirical models can assist to eliminate unnecessary fungicide applications by recommending control action only when conditions are favorable, thus providing robust information to assist growers (Shtienberg, 2013).

Mathematical models that represent the dynamics of diseases have been linked to DSS aiming to predict the risk of occurrence and severity of diseases along the season and recommend the need for a fungicide application (Fernandes et al., 2007). Previous studies have reported the use of DSSs to predict the risk of occurrence of diseases such as anthracnose fruit rot and botrytis fruit rot in strawberries (Pavan et al., 2011), apple fire blight and downy mildew in grapevines (Sremac et al., 2018), orange rust in sugarcane (Lemes Guedes et al., 2013), fusarium head blight in wheat (Landschoot et al., 2013), and late blight in tomatoes and potatoes (Small et al., 2015). All these investigators reported successful contributions of DSSs to control and manage plant diseases in different regions of the world. DSS can improve the performance of decision makers, such as grower’s and extension-agents, and reduce the human and resources needed to analyze complex and large amount of data (Jones, 1993).

Based on the importance of improving the control and management of PFD in citrus production, the main objective of this study was to develop a user-friendly DSS to monitor and forecast the risk of PFD occurrence in citrus groves. The Citrus advisory system (CAS) was developed to help growers in the state of Florida improve spray decisions and spray fungicide only when conditions are favorable for PFD occurrence, thus lowering the risk of production losses associated with the disease during the growing season as wells as decreasing production cost and environmental impacts.

Section snippets

Material and methods

The CAS DSS was developed to estimate the risk of PFD occurrence on citrus based on weather observations across the state of Florida in order to improve disease management. In this section, the weather data sources, leaf wetness models, and the leaf wetness decision algorithm are described. Finally, we present how information technologies are used to provide a solution that allows users to easily access the system.

Results and discussion

The DSS in this study, named citrus advisory system (CAS), was implemented, refined and made available in the AgroClimate website (http://agroclimate.org) (Fraisse et al., 2015a, Fraisse et al., 2006). This DSS can be found on the AgroClimate website, in the section “Crop Diseases”.

The CAS web pages are presented in Fig. 4. All the weather stations present their risk within three different colors: green (low risk), yellow (moderate risk), and red (high risk). The weather stations are updated

Conclusions

We introduced the citrus advisory system, a web-based tool to monitor and estimate PFD risks on citrus in Florida. This system easily allows farmers to monitor daily PFD risks in their area and receive relevant information for fungicide applications. Growers using this system can use the information to apply fungicides when needed and reduce the number of fungicide applications when conditions are not conducive for the disease, consequently reducing the costs related to fungicide sprays.

Future

CRediT authorship contribution statement

Daniel Perondi: Writing - original draft, Software, Data curation. Clyde W. Fraisse: Supervision, Conceptualization, Writing - review & editing. Megan M. Dewdney: Conceptualization, Funding acquisition, Investigation. Vinícius A. Cerbaro: Software. José H. Debastiani Andreis: Software. André B. Gama: Writing - review and editing, Investigation. Geraldo J. Silva Junior: Conceptualization, Writing - review and editing, Methodology. Lilian Amorim: Writing - review and editing, Investigation.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This research was supported by the Citrus Research and Development Foundation (Grant number 16-010C).

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