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

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

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

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

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

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

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

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

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

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

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