Nowadays, peach farms, and agriculture in general, face intensified challenges linked to pest control and irrigation needs, due to the effects of climate change. A contemporary and effective approach to these challenged is presented herein, which is based on the utilization of a smart farming system and its specialization in peach farms in Greece, in the framework of the SmartPeach project.

The proposed smart farming system is based on specialized forecasting models that optimize irrigation and predict diseases to rationalize the use of plant protection products. It utilizes heterogeneous information sources including agro-environmental sensing stations, Earth Observation services (mainly Sentinel-1 and Sentinel-2 Copernicus missions), farmer’s digital calendar, and on-the-field observations of the cultivation. The most important advantage of the specific Smart Farming System is its service based approach. Smart Farming is offered as a Service and so its application does not require technological investments from the farmers.

SmartPeach is an on-going project, expected to be completed in June of 2022. The current research indicates a potential reduction of up to 25% on total production cost due to optimization of irrigation and pest control applications, as well as an increase in farmers’ income up to 10% linked to improved product quality and yield.

In addition to the significant economic benefits from the implementation of the proposed Smart Farming System, the enhanced adaptation of the farms to the ever-changing climatic conditions, due to climate change, and the minimization of the ecological footprint of the farms are of crucial importance and worth emphasizing.

Almond is one of the major nuts worldwide, mainly due to the recognized health benefits provided by their ingestion. Almond is often eaten raw or after undergoing some processing procedures that can change chemical attributes. The present work was carried out to provide information on the effects of roasting and blanching on the contents of bioactive compounds and antioxidant activities of Portuguese almond cultivars (Casanova, Molar, Pegarinhos and Refêgo), comparing them to two foreign cultivars (French cultivar Ferragnès and Spanish cultivar Glorieta). Overall, results show that levels of bioactive compounds (total phenolics and total flavonoids) and antioxidant activities increased with roasting, and decreased with blanching, but with considerable differences dependent of the cultivar. This work offers some new data regarding neglected Portuguese almond cultivars, that might be of interest to the scientific community, growers, and consumers, hence motivating efforts for genotype conservation and fruit valorization.

Sweet cherry is a highly appreciated fruit, with great economic value, linked not only to production yield but also to quality and beneficial health effects, influencing consumer acceptability. Biostimulants have arrived as innovative agronomical strategies to increase the yield and fruit quality of sweet cherries. This work intends to highlight the effect of the application of two concentrations of seaweed-based (SW) and glycine betaine (GB) biostimulants and their combination, on quality (size, soluble solids content, maturity index, and sensorial characteristics) and antioxidant traits (DPPH, FRAP, and β-carotene). Sweet cherries were harvested from a commercial sweet cherry orchard, grafted on “Saint Lucia” rootstock, located in Resende, Northern Portugal. Biostimulants significantly affected (p < 0.01) fruit size, TSS, and SSC/TA: sweet cherries present higher dimensions, soluble solids content, and an increased maturity index when treated with SW at the highest concentration. The application of biostimulants also improved the sensorial profiles of sweet cherries. Overall appearance and firmness attribute enhanced when fruits were sprayed with both SW treatments, while cherry flavour attribute was stronger with SW at highest concentration and MIX treatment. In addition, cherries treated with Mix treatment had a higher antioxidant capacity.

Improving the rate of germination and crop growth in early season via increasing crop competitiveness can increase the competitive ability of crops with weeds through enhancing physiological traits. In order to investigate the effect of cold plasma treatment on some physiological and quality traits of sunflower plants in completion with weeds, an experiment was carried out as a factorial in a randomized complete block design with three replications at Shahrood University of Technology in 2016. Experimental factors included cold plasma at six levels: control, hydro-priming of seeds for 10 hours, pretreatment of seeds with cold plasma radiation for 15 and 30 seconds, hydro-priming of seeds for 10 hours + cold plasma radiation for 15 and 30 seconds and weed control at three levels: control (no weeding), weeding all season and application of trifluralin (1200 g. ai. ha^-1). Dielectric barrier discharge plasma jet was operated in ambient air under sterile conditions. After seed priming in distilled water for 10 hours, sunflower seeds were taken in petri plates and treated with the plasma for determined times. Results showed that membrane stability index and carotenoid increased by cold plasma and hydro-priming treatments in weed free than weeds infested conditions. Total chlorophyll content increased by 8.87 and 7.74% in weeding and herbicide application, respectively, compared with no weeding treatments. Sunflower seeds protein percentage increased significantly by application of hydro-priming + cold plasma radiation for 30 seconds compared with herbicide application treatment. Sunflower seed oil percent also increased by using cold plasma radiation for 15 and 30 seconds in weed infested treatments compared with weed free and herbicide application condition. Weed density and biomass decreased significantly by plasma compared with control treatment and the most decrease was observed in cold plasma radiation treatment for 15 and 30 seconds. Based on our results, pre-treatment of seeds by cold plasma and hydro-priming could significantly improve some physiological and quality traits of sunflower through increasing of the crop competitive ability with weeds.

The increasing population of the world led to a growing need to provide food for humans and livestock. So, the potential use of saline soils in agriculture becomes obvious. Salicornia is from the Chenopodiaceae family and is a good candidate for introduction to saline and semi-saline soils. This study aimed to investigate the effect of different salinity concentrations on germination parameters of two species of Salicornia. A factorial experiment was conducted in a completely randomized design with three replications in the environmental stress laboratory of Sari Agricultural Sciences and Natural Resources University, Iran. Experimental treatments included eight salinity levels (0, 50, 100, 200, 300, 400, 500, and 700 mM NaCl) and two species of Salicornia (S. persica and S. perspolitana). After disinfecting the seeds and applying the treatments, seed germination was counted for 10 days and finally, the parameters of coefficient of germination rate, average daily germination, germination index, and germination uniformity were calculated. The results showed that with increasing salinity concentration to the level of 144.9 mM, the germination index increased, while with a further increase in salinity from this level, the germination index showed a decreasing trend. The germination rate in S. persica was significantly higher than S. Persepolitana, about 22%. Also, in S. Persepolitana, daily germination decreased linearly with increasing average salinity, while in S. Persica species, this parameter increased from zero to 50 mM salinity level. To sum up, the responses of germination indices in S. persica were better than S. Persepolitana at an equal salinity and probably S. persica tolerates higher salinity at the germination stage.

Field experiment was carried at Experimental Station of Vytautas Magnus University Agriculture Academy in 2019–2020, Lithuania. The aim of the study was to determine and to compare weed spread and caraway (Carum carvi L.) crop productivity in sole (spring barley, spring wheat, pea, caraway), binary (spring barley-caraway, spring wheat-caraway, pea-caraway) and trinary (spring barley-caraway-white clover, spring wheat-caraway-white clover, pea-caraway-white clover) crops. We hypothesized that the application of a multi-cropping system would inhibit the spread of weeds and increase the productivity of caraway crop. Objectives: 1) to determine the weed species composition in the multi-cropping (sole, binary, and trinary) system; 2) to determine the number and dry biomass of weed in multi-cropping (sole, binary, trinary) system; 3) to evaluate the yield of caraway seeds grown in multi-cropping system.

Dry biomass of weeds was established before harvesting the main crop (spring barley, spring wheat, and pea) (2019), and during the second year (2020) of caraway vegetative season —before harvesting caraway and spring barley in 10 randomly selected sites of 0.06 m² in each harvested plot. The number and species composition of the weeds were determined in the laboratory, and the weeds were dried in an oven at 60 °C and weighed. The number of weeds was recalculated to pcs. m⁻² and the dry biomass to g m⁻². Caraway seed yield calculation was based on a standard 12% moisture content and absolutely clean seed content (t ha⁻¹).

In the first year of caraway vegetative season (2019), 23 weed species were found in multi-crops, including 19 annual weeds and 4 perennial ones. The weeds found belong to 11 different families. Predominated annual dicotyledonous weed species: white goosefoot (Chenopodium album L.) and scentless chamomile (Tripleurospermum perforatum (Merat) M. Lainz). In the second year of caraway vegetative season (2020), 22 weed species were found in multi-crops, including 18 annual weeds and 4 perennial ones. The weeds found belong to 11 different families. Scentless chamomile (Tripleurospermum perforatum (Merat) M. Lainz) and common dandelion (Taraxacum officinale F.H. Wigg.) were dominant in binary and trinary crops. Significantly from 1.7 to 3.7 times higher yield of caraway seed was formed when it was grown after pea, compared to other crops.

Agricultural land-use and effects on water quality (surface and groundwater) is a well known issue and actions are needed to reduce the impacts of farm inputs management. Direct and indirect links can be found with some of the SDGs such as SDG 6 - Clean water and sanitation and SDG 15 Life on land. Assessing these impacts can support the definition of sustainable management practices for agricultural production.

In this work we performed an analysis of the relationship between agricultural land-use and pesticides found in surface water monitoring stations located in South Italy (Foggia province).

Land-use data were produced with a complex data integration process based on the use of geospatial data from the Italian agricultural paying agency. These administrative data support the process for assigning European agricultural subsidies to farms and are made up of land-use maps and farm crops declaration at parcel level. The geospatial administrative data were integrated ensuring a consistent definition of the geometric and nomenclature characteristics (e.g. topology checks, semantic harmonisation of crops and land use classes).

A subset of 26 monitoring stations located in the main watershed of the study area were acquired from the georeferenced database on water quality from the Italian environmental agency. Among the active substances monitored, only those commonly related to farm inputs products were extracted. Specifically, we selected Isoproturon, a plant protection chemical, present in surface waters.

This methodology was followed: polygons within a 5km buffer area around the monitoring station were selected, rasterized and assigned to the respective Isoproturon concentration level. Then, the raster was converted into point data and the frequency matrix of land-use and concentration was used to perform a Correspondence Analysis.

The results, plotted in a symmetric map, show the variation of concentration level among the different land-uses. Specifically, low concentration levels are usually associated with agricultural polygons located within ecological focus areas as defined by the European policies.

Rice fields and several activities in rice cultivation processes are a source of greenhouse gas (GHG) emissions. The activities of rice straw burning, field preparation by tractor, irrigation by water pump, planting by machine, flooding during rice growing season, fertilization, and harvest by machine lead to different environmental impacts. This study aimed to assess the GHG emissions and carbon footprint (C-footprint) of rice cultivation in the local community in Thailand. The SMART GHG mobile application (SGA) was used to calculate GHG emissions in each cultivation activity and the C-footprint of paddy rice production. Activity data was obtained from 71 farmer households with 134 ha of cultivated areas in Muang Chang Sub-district of Nan province, Suantaeng Sub-district of Suphanburi province and Nakham Sub-district of Nakorn Phanom provinces. From the data input of the application, GHG emissions from rice cultivation practice accounted for 5.99 t CO₂e/ha. The emission of CH₄ during cultivation was the highest (83.4% of the net total) while the emission from fertilization, field preparation by machine, harvest by machine, and residue burning shared by 5.4, 4.4, 3.8 and 2.0% of the net total, respectively. The mobile application can also report C-footprint of rice yields in Muang Chang, Suantaeng and Nakham Sub-districts by 1.77, 1.10 and 1.09 kg CO2e/kg yield. It was found that the SGA can track and demonstrate well analysis of seasonal GHG emissions and C-footprint, which can develop to be a baseline and emission reduction for low-carbon rice production of Thailand.

The present study was conducted to evaluate and compare the chemical composition and bio activity on different insect organisms of essential oils from four Albanian coniferous plants. The phytochemical analysis carried out using GC-MS showed that the oils were constituted mainly by monoterpenes, sesquiterpenes and diterpenes. Chemical analysis identified 16 constituents in Black pine and Silver fir, 17 constituents in Dauglas fir, while the analysis allowed identification of 28 constituents in Juniperus berry. Main costituents of the essential oils were α-pinene and c-verbenol in Black pine; β-pinene, α-fenchone and α-pinene in Douglas fir; limonene, β-pinene, α-pinene, and camphene in Silver fir; α-pinene, sabinene, β-myrcene in Juniperus berry. The oils showed varying degree of insecticidal activity. Juniperus berry and Silver fir affected the settling behavior of the aphids Myzus persicae and Rhopalosiphum padi respectively. Black pine oil presented significant activity against the tick Hyalomma lusitanicum and the root-knot nematode Meloidogyne javanica. The ixodicidal effect of this essential oil was explained by its content in c-verbenol, while this compound and binary combinations of α-pinene and c-verbenol were not nematicidal, suggesting synergic effects between minor components of Black pine essential oil.

There is a growing need for remote identification of the crop types, which is directly related to vegetation density and it is a valuable tool for agricultural inspectors and government agencies. The serious issue that has arisen in recent years for policy makers and statistical accountants is the degree of validity of information concerning the type and area of each crop. Information on the spatial distribution of arable land and crop types can also assist in the accurate statistical estimation of crop area and the improval of agricultural policy planning. In this study remote sensing data was utilized by Sentinel-2 mission, from which NDVI values were calculated based on Band 4 (0.665μm) and Band 8 (0.842μm) for the period 2017-2020. Three different crops were analyzed: cotton, rice and olive trees. The data used for the experiment were preprocessed and monthly average NDVI values were calculated for each month. Preprocess included the typical method of average. Next, a machine learning algorithm was developed and training was accomplished utilizing monthly average NDVI values. Python programming language and KNN machine learning module on a Pycharm shell were used for the development of the machine learning algorithm. The algorithm was saved on a joblib file when training was completed. Subsequently, a second algorithm was developed for inserting used defined NDVI values and processing using the trained algorithm (joblib). Through this process, the identification of crop types was accomplished based on NDVI values, through the algorithm that has been developed. From the literature so far, it is concluded that a machine learning system can respond to crop recognition learning and further identification.