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A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Innovative Cropping Systems".

Deadline for manuscript submissions: closed (31 August 2019) | Viewed by 56172

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Special Issue Editors

Prof. Dr. Andrea Sciarretta

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Guest Editor

Department of Agriculture, Environmental and Food Sciences, University of Molise, Via de Sanctis 1, I-86100 Campobasso, Italy
Interests: lepidoptera; fruit fly; IPM; precision farming; agro-ecology; geostatistics; smart agriculture; monitoring; trapping; fruit crops
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Prof. Gianni Gilioli

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Guest Editor

Department of Molecular and Translational Medicine, University of Brescia, Viale Europa 11, I-25123 Brescia, Italy
Interests: development and implementation of simulation models and software packages to support pest management and agricultural production activities; human health and sustainable development in Sub-Saharan Africa; risk analysis and food safety

Special Issue Information

Dear Colleagues,

Information Technology (IT) is one of the main drivers of innovation in agriculture. The tools to collect and process data and information are elements of the technological infrastructure supporting the design and implementation of crop management responses at the appropriate space and time, according to the principles of IPM and Precision Agriculture, and more recently of Agriculture 4.0. The widespread use of sensors networks, communication devices and computers together with simulation software allow to manipulate and to analyze large amount of data related to pests, crops, environmental driving variables and landscape. All these are components of Decision Support Systems (DSSs), which are key elements in precision crop protection. Graphical interfaces make DSS simple and efficient for the end users, assisting them in accelerating the data entry process, generating reports, guiding in field and food-chain management decisions, providing risk maps, and running complex simulation scenarios to direct management tactics, strategies and policies. The possibility of remotely accessing and using, via the web, DSSs enlarges the potentiality of such technology for the service providers and users.
With this Special Issue, we intend to explore the main issues of ITs specifically designed to protect crops and food production against pests and diseases, with particular attention on, but not limited to, spatial and temporal population dynamics, epidemiological models, site-specific IPM, DSS development and applications, and the Internet of things.

Prof. Andrea Sciarretta
Prof. Gianni Gilioli
Guest Editors

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Keywords

Precision agriculture
Integrated pest management
Decision support system
Epidemiological modelling
World wide web
Sensors

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Published Papers (11 papers)

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23 pages, 7829 KiB

Open AccessArticle

Developing and Implementation of Decision Support System (DSS) for the Control of Olive Fruit Fly, Bactrocera Oleae, in Mediterranean Olive Orchards

by Miguel Ángel Miranda, Carlos Barceló, Ferran Valdés, José Francisco Feliu, David Nestel, Nikolaos Papadopoulos, Andrea Sciarretta, Maurici Ruiz and Bartomeu Alorda

Agronomy 2019, 9(10), 620; https://doi.org/10.3390/agronomy9100620 - 9 Oct 2019

Cited by 22 | Viewed by 5447

Abstract

Modern agriculture requires technology to give precise measures about relevant parameters such as pest control. Here, we developed a decision support system (DSS) based on semi-automatic pest monitoring for managing the olive fruit fly Bactrocera oleae (Rossi), in Mallorca (Balearic Islands, Spain). The DSS was based on an algorithm that took into account spatial and temporal patterns of olive fruit fly population in an orchard where all trees were georeferenced, thus precise treatments against the pest were conducted through a location aware system (LAS). The olive fruit fly adult population was monitored by using ad hoc off-the-grid autonomous electronic traps.The results were compared with those obtained with conventional methods. For a pilot trial, we selected an olive-producing orchard, where from June to October 2015, three plots using LAS management and three plots under conventional control (NO-LAS plots) were compared. Spray threshold considered both adult population and fruit damage. An additional non-sprayed plot was selected for assessing biological control due to the parasitoid, Psyttalia concolor (Szépligeti). Results showed that the use of DSS reduced by 36.84% the volume of insecticide used in LAS compared to NO-LAS plots. Accordingly, time and distance needed for spraying were also reduced. Adult olive fruit fly population was lower in the LAS plots when compared with the NO-LAS plots; conversely, fruit infestation was higher in LAS compared with NO-LAS. The implementation of LAS and DSS at field level allowed real-time monitoring of adult olive flies, thereby increasing the accuracy and precision of sprays in time and space and decreasing impact on natural enemies. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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14 pages, 3513 KiB

Open AccessArticle

Defining and Evaluating a Decision Support System (DSS) for the Precise Pest Management of the Mediterranean Fruit Fly, Ceratitis capitata, at the Farm Level

by Andrea Sciarretta, Maria Rosaria Tabilio, Armando Amore, Marco Colacci, Miguel Á. Miranda, David Nestel, Nikos T. Papadopoulos and Pasquale Trematerra

Agronomy 2019, 9(10), 608; https://doi.org/10.3390/agronomy9100608 - 2 Oct 2019

Cited by 16 | Viewed by 4526

Abstract

A Decision Support System (DSS) was developed and evaluated to control the Mediterranean fruit fly (medfly), Ceratitis capitata (Wiedermann), by incorporating a semi-automatic pest monitoring and a precision targeting approach in multi-varietal orchards. The DSS consists of three algorithms. DSS1, based on the degree days calculation, defines when the traps should be deployed in the field initiating the medfly population monitoring. DSS2 defines the areas to be treated and the type of treatment based on the number of adult medfly captures, harvesting time, and phenological stage of the host cultivar. DSS3 defines the spraying procedure considering the technical registration properties of the selected insecticide (e.g., withholding period and efficacy duration time) and weather conditions. The DSS was tested in commercial orchard conditions near Rome, central Italy, with a randomized complete blocks experimental design, comparing DSS-assisted and conventional management. In the DSS-assisted plots, a semi-automatic adult medfly monitoring system was deployed, composed of real-time, wireless electronic traps. The output of the functioning DSS is a map of spraying recommendation, reporting the areas to be treated and the treatment type (bait or cover insecticide spraying). The farmer was left free to follow, or not, the DSS indications. The first medfly captures were observed on June 30, whereas the DD threshold was reached on July 3 when the DSS started to operate. The field test produced 29 DSS decisions from July 3 to September 1 and confirmed that medfly management using the DSS substantially reduced the number of pesticide applications, the treated area, and the volumes of pesticide utilization. No significant differences in infested fruit were observed between DSS-assisted and conventional management. The level of acceptance of the DSS by the farmer was 78%. This evidence confirmed the requirement of fully involving farmers and pest managers during the evaluation process of DSS. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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14 pages, 2267 KiB

Open AccessArticle

Soil Biological Quality Assessment to Improve Decision Support in the Wine Sector

by Isabella Ghiglieno, Anna Simonetto, Pierluigi Donna, Marco Tonni, Leonardo Valenti, Floriana Bedussi and Gianni Gilioli

Agronomy 2019, 9(10), 593; https://doi.org/10.3390/agronomy9100593 - 28 Sep 2019

Cited by 12 | Viewed by 3878

Abstract

Biodiversity is an increasingly important aspect of wine production. The assessment of agro-ecosystem biodiversity is highly complex due to the heterogeneity of the elements involved in the evaluation. For this reason, wine companies have expressed a need for a decision support system (DSS) capable of dealing with this complexity, integrating assessments referring to the whole production system within a single tool. In this study a DSS developed for wine sector biodiversity management assessment is introduced. The DSS, called BIOPASS^®, is made up of different sections relating to three compartments in the winemaking process (the soil, the vine and wine). Assessment of the physical, chemical and biological components of soil is a key element of the DSS. We investigate the relationship between biological soil quality (represented by the QBS-ar index), environmental conditions and the type of farming (organic or conventional). 70 soil samples were analysed in different Italian viticultural contexts. The model highlighted the relationships between QBS-ar and meteorological variables (air temperature and precipitation) as well as a positive relationship with organic farming systems. These results provide useful information for understanding agroecosystem biodiversity and will be integrated within the DSS for assessment of soil quality. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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13 pages, 2004 KiB

Open AccessArticle

Interrupted Wet Period (IWP) to Forecast the Aerial Alternaria in Potato Crops of A Limia (Spain)

by Laura Meno, Olga Escuredo, María Shantal Rodríguez-Flores and María Carmen Seijo

Agronomy 2019, 9(10), 585; https://doi.org/10.3390/agronomy9100585 - 26 Sep 2019

Cited by 10 | Viewed by 3837

Abstract

Potato early blight caused by Alternaria solani generates significant economic losses in crops worldwide. Forecasting the risk of infection on crops is indispensable for the management of the fungal disease, ensuring maximum economic benefit but with minimal environmental impact. This work aimed to calculate the interrupted wet periods (IWP) according to the climate conditions of A Limia (Northwest of Spain) to optimize the prediction against early blight in potatoes. The study was performed during nine crop cycles. The relative hourly humidity and Alternaria concentration in the crop environment were taken into account. Alternaria levels were monitored by aerobiological techniques using a LANZONI VPPS-2000 volumetric trap. The relationships between weather conditions and airborne Alternaria concentration were statistically analyzed using Spearman correlations. To establish the effectiveness of wetness periods, the first important Alternaria peak was taken into account in each crop cycle (with a concentration greater than 70 spores/m³). Considering the six interrupted wet periods of the system, it was possible to predict the first peak of Alternaria several days in advance (between 6 and 38 days), except in 2007 and 2018. Automated systems to predict the initiation of early blight in potato crop, such as interrupted wet periods, could be an effective basis for developing decision support systems. The incorporation of aerobiological data for the calculation of interrupted wet periods improved the results of this system. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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19 pages, 5377 KiB

Open AccessArticle

Development and Field Testing of a Spatial Decision Support System to Control Populations of the European Cherry Fruit Fly, Rhagoletis cerasi, in Commercial Orchards

by Charalampos S. Ioannou, Stella A. Papanastasiou, Kostas D. Zarpas, Miguel Angel Miranda, Andrea Sciarretta, David Nestel and Nikos T. Papadopoulos

Agronomy 2019, 9(10), 568; https://doi.org/10.3390/agronomy9100568 - 20 Sep 2019

Cited by 6 | Viewed by 3632

Abstract

The European cherry fruit fly, Rhagoletis cerasi (Diptera: Tephritidae), is a key pest for the cherry production industry in Europe and west Asia that has recently invaded North America. Insecticide applications are frequently employed to control this devastating pest, often without considering its population trends. We developed a novel decision support system (DSS), and field tested it in commercial sweet cherry orchards in central Greece. The DSS includes two algorithms that predict the timing of adult activity in the wild and support pest management decisions, based on R. cerasi population trends and pesticide properties, respectively. Preparatory monitoring of the testing area during 2014, using adult traps, revealed high population densities of R. cerasi in non-managed sweet cherry orchards and low densities in commercial ones. Implementation of the DSS during 2015 resulted in low R. cerasi adult population densities and zero fruit infestation rates in commercial cherry orchards. Similar population and infestation rates were recorded in conventionally treated plots that received on average two insecticide applications compared to the one-half that the DSS treated plots received. Simultaneously, high population densities and fruit infestation rates were recorded in non-managed cherry orchards. Apparently, the implementation of the simple DSS we developed reduces the cost of R. cerasi management and minimizes the chemical footprint on both the harvested fruit and the environment. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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16 pages, 2035 KiB

Open AccessArticle

Does Harvesting Affect the Spatio-Temporal Signature of Pests and Natural Enemies in Alfalfa Fields?

by Mahsa Ghahramani, Roghaiyeh Karimzadeh, Shahzad Iranipour and Andrea Sciarretta

Agronomy 2019, 9(9), 532; https://doi.org/10.3390/agronomy9090532 - 11 Sep 2019

Cited by 8 | Viewed by 2831

Abstract

Determining the spatio-temporal distribution and association of pests and natural enemies would be useful for implementing biological control of pests and could also be used in site-specific pest management. In this study, the spatio-temporal distribution and association of aphids, plant bugs, and natural enemies were assessed in alfalfa fields using geo-statistics and spatial analysis by distance indices (SADIE). Additionally, the effect of alfalfa hay-harvesting on the spatial and temporal distribution of these insects was investigated for the first time. Geostatistical analysis indicated that the degree of dependence (DD) was ≥75% for 11 out of 39, 9 out of 35, 3 out of 12, 10 out of 29, and 2 out of 20 datasets for pea aphid Acyrthosiphon pisum, spotted alfalfa aphid Therioaphis maculata, cowpea aphid Aphis craccivora, alfalfa plant bug Adelphocoris lineolatus, and tarnished plant bug Lygus rugulipennis, respectively. The results also indicated that DD was ≥75% in 7 out of 45, 18 out of 45, and 3 out of 20 datasets for Coccinella septempunctata, Hippodamia variegata, and Pterostichus melanarius, respectively. Harvesting decreased the aggregation of the ladybirds, which resulted in a decrease in the index of aggregation. The geo-statistics results were confirmed by SADIE in 75% of datasets. These results can be used in biological control and site-specific management of aphids and plant bugs in alfalfa fields. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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17 pages, 5226 KiB

Open AccessFeature PaperArticle

Machine Learning-Based Spectral Library for Crop Classification and Status Monitoring

by Jingcheng Zhang, Yuhang He, Lin Yuan, Peng Liu, Xianfeng Zhou and Yanbo Huang

Agronomy 2019, 9(9), 496; https://doi.org/10.3390/agronomy9090496 - 29 Aug 2019

Cited by 47 | Viewed by 6945

Abstract

The establishment and application of a spectral library is a critical step in the standardization and automation of remote sensing interpretation and mapping. Currently, most spectral libraries are designed to support the classification of land cover types, whereas few are dedicated to agricultural remote sensing monitoring. Here, we gathered spectral observation data on plants in multiple experimental scenarios into a spectral database to investigate methods for crop classification (16 crop species) and status monitoring (tea plant and rice growth). We proposed a set of screening methods for spectral features related to plant classification and status monitoring (band reflectance, vegetation index, spectral differentiation, spectral continuum characteristics) that are based on ISODATA and JM distance. Next, we investigated the performance of different machine learning classifiers in the spectral library application, including K-nearest neighbor (KNN), Random Forest (RF), and a genetic algorithm coupled with a support vector machine (GA-SVM). The optimal combination of spectral features and the classifier with the highest classification accuracy were selected for crop classification and status monitoring scenarios. The GA-SVM classifier performed the best, which produced an accuracy of OAA = 0.94, Kappa = 0.93 for crop classification in a complex scenario (crops mixed with 71 non-crop plant species), and promising accuracies for tea plant growth monitoring (OAA = 0.98, Kappa = 0.97) and rice growth stage monitoring (OAA = 0.92, Kappa = 0.90). Therefore, the establishment of a plant spectral library combined with relevant feature extraction and a classification algorithm effectively supports agricultural monitoring by remote sensing. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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14 pages, 3574 KiB

Open AccessArticle

An Integrated Decision Support System for Environmentally-Friendly Management of the Ethiopian Fruit Fly in Greenhouse Crops

by David Nestel, Yafit Cohen, Ben Shaked, Victor Alchanatis, Esther Nemny-Lavy, Miguel Angel Miranda, Andrea Sciarretta and Nikos T. Papadopoulos

Agronomy 2019, 9(8), 459; https://doi.org/10.3390/agronomy9080459 - 15 Aug 2019

Cited by 9 | Viewed by 4946

Abstract

The Ethiopian fruit fly (EFF), Dacus ciliatus, is a key, invasive pest of melons in the Middle East. We developed and implemented a novel decision support system (DSS) to manage this pest in a greenhouse environment in Southern Israel. Dacus ciliatus is commonly controlled in Israel with repeated calendar-sprayings (every 15 days) of pyrethroid pesticides. The current study compares the performance of a DSS against calendar-spraying management (CSM). DSS was based on EFF population monitoring and infestation. DSS took into consideration concerns and observations of expert managers and farmers. During 2014, EFF damage was concentrated in the spring melon production season. Fall and winter production did not show important damage. Damage during the spring of 2014 started to increase when average EFF/trap/day reached 0.3. This value was suggested as the threshold to implement pesticide spraying in DSS greenhouses. EFF/trap/day trends were derived from monitoring with conventional traps and a novel electronic remote sensing trap, developed by our group. CSM during the spring of 2015 included 3 EFF control sprays, while DSS-managed greenhouses were only sprayed once. At the end of the spring season, damage was slightly higher in DSS greenhouses (1.5%), but not significantly different to that found in CSM greenhouses (0.5%). Results support continuing DSS research and optimization to reduce/remove pesticide use against EFF in melon greenhouses. Interactions with farmers and managers is suggested as essential to increase adoption of DSS in agriculture. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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17 pages, 1141 KiB

Open AccessArticle

Methods for the Identification of Microclimates for Olive Fruit Fly

by Romanos Kalamatianos, Ioannis Karydis and Markos Avlonitis

Agronomy 2019, 9(6), 337; https://doi.org/10.3390/agronomy9060337 - 25 Jun 2019

Cited by 3 | Viewed by 3576

Abstract

The support and development of the primary agri-food sector is receiving increasing attention. The complexity of modern farming issues has lead to the widespread penetration of Integrated Pest Management (IPM) Decision Support Systems (DSS). IPM DSSs are heavily dependent on numerous conditions of the agro-ecological environment used for cultivation. To test and validate IPM DSSs, permanent crops, such as olive cultivation, are very important, thus this work focuses on the pest that is most potentially harmful to the olive tree and fruit: the olive fruit fly. Existing research has indicated a strong dependency on both temperature and relative humidity of the olive fruit fly’s population dynamics but has not focused on the localised environmental/climate conditions (microclimates) related to the pest’s life-cycle. Accordingly, herein we utilise a collection of a wide-range of integrated sensory and manually tagged datasets of environmental, climate and pest information. We then propose an effective and efficient two-stage assignment of sensory records into clusters representing microclimates related to the pest’s life-cycle, based on statistical data analysis and neural networks. Extensive experimentation using the two methods was applied and the results were very promising for both parts of the proposed methodology. The identified microclimates in the experimentation were shown to be consistent with intuitive and real data collected in the field, while their qualitative evaluation also indicates the applicability of the proposed method to real-life uses. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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14 pages, 7350 KiB

Open AccessArticle

Spatial and Temporal Trends of Irrigated Cotton Yield in the Southern High Plains

by Wenxuan Guo

Agronomy 2018, 8(12), 298; https://doi.org/10.3390/agronomy8120298 - 8 Dec 2018

Cited by 7 | Viewed by 4162

Abstract

Understanding spatial and temporal variability patterns of crop yield and their relationship with soil properties can provide decision support to optimize crop management. The objectives of this study were to (1) determine the spatial and temporal variability of cotton (Gossypium hirsutum L.) lint yield over different growing seasons; (2) evaluate the relationship between spatial and temporal yield patterns and apparent soil electrical conductivity (EC_a). This study was conducted in eight production fields, six with 50 ha and two with 25 ha, on the Southern High Plains (SHP) from 2000 to 2003. Cotton yield and EC_a data were collected using a yield monitor and an EC_a mapping system, respectively. The amount and pattern of spatial and temporal yield variability varied with the field. Fields with high variability in EC_a exhibited a stronger association between spatial and temporal yield patterns and EC_a, indicating that soil properties related to EC_a were major factors influencing yield variability. The application of EC_a for site-specific management is limited to fields with high spatial variability and with a strong association between yield spatial and temporal patterns and EC_a variation patterns. For fields with low variability in yield, spatial and temporal yield patterns might be more influenced by weather or other factors in different growing seasons. Fields with high spatial variability and a clear temporal stability pattern have great potential for long-term site-specific management of crop inputs. For unstable yield, however, long-term management practices are difficult to implement. For these fields with unstable yield patterns, within season site-specific management can be a better choice. Variable rate application of water, plant growth regulators, nitrogen, harvest aids may be implemented based on the spatial variability of crop growth conditions at specific times. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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Review

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19 pages, 552 KiB

Open AccessReview

Critical Success Factors for the Adoption of Decision Tools in IPM

by Vittorio Rossi, Giorgio Sperandio, Tito Caffi, Anna Simonetto and Gianni Gilioli

Agronomy 2019, 9(11), 710; https://doi.org/10.3390/agronomy9110710 - 3 Nov 2019

Cited by 72 | Viewed by 10809

Abstract

The rational control of harmful organisms for plants (pests) forms the basis of the integrated pest management (IPM), and is fundamental for ensuring agricultural productivity while maintaining economic and environmental sustainability. The high level of complexity of the decision processes linked to IPM requires careful evaluations, both economic and environmental, considering benefits and costs associated with a management action. Plant protection models and other decision tools (DTs) have assumed a key role in supporting decision-making process in pest management. The advantages of using DTs in IPM are linked to their capacity to process and analyze complex information and to provide outputs supporting the decision-making process. Nowadays, several DTs have been developed, tackling different issues, and have been applied in different climatic conditions and agricultural contexts. However, their use in crop management is restricted to only certain areas and/or to a limited group of users. In this paper, we review the current state-of-the-art related to DTs for IPM, investigate the main modelling approaches used, and the different fields of application. We also identify key drivers influencing their adoption and provide a set of critical success factors to guide the development and facilitate the adoption of DTs in crop protection. Full article

(This article belongs to the Special Issue Information Technologies for Precision Plant and Crop Protection)

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