Using a GIS technology to plan an agroforestry sustainable system in Sardinia

This study was conducted with the aim to quantify the spread of livestock agroforestry in a Mediterranean ecosystem (island of Sardinia, Italy) and evaluate its sustainability in terms of grazing impact. By using GIS software ArcMap 10.2.2, the map of Sardinia vegetal landscape, obtained by information of Sardinia nature map based on the classification of habitat according to CORINE-Biotopes system, have been overplayed with the map of livestock grazing impact map CAIA developed by INTREGA (spin-off ENEA), to obtain for Meriagos (local agro-silvo-pastoral systems; classified “Dehesa 84.6” according to CORINE-Biotopes system), bushlands and woodlands, the surfaces under grazing and evaluate the extension of overgrazing for each of them

Технические системы цифрового контроля качества обработки почвы

The production of tillage equipment is focused on the growing use of soil-protective and resource-saving farming and the use of the precision agriculture in tillage principles. The differentiated tillage concept arose, and occupied an intermediate position between traditional and anti-erosion (shallow) types of tillage. The authors conducted an analysis of technical systems for tillage quality digital control taking into account the indicated trends. They indicated that there was a certain inconsistency in the soil cultivation systems names in the scientific literature. (Research purpose) To provide an analytical overview of the tillage digital quality control technical systems. (Materials and methods) The authors used manufacturers’ brochures of tillage equipment, patents and scientific works. (Results and discussion) The authors examined the commercial offers existing in the world market in the differentiated tillage and digital quality control systems spheres. They presented an analysis of similar solutions available in the world scientific literature. They studied the issues of controlling the angle of disks’ attack, the depth of tillage, the soil surface ridging, the average size of the soil lumps, the amount of crop residues, determining the soil properties in a non-contact way. They identified the fragmentation of scientific and industrial developments in the sphere of tillage quality control. They suggested combining them into one system to automate the process of differentiated tillage. (Conclusions) It was shown that tillage equipment is becoming more adaptable in terms of meeting the specific requirements of the farmer for tillage. The authors identified promising areas for the future development of tillage machines: the inclusion of different subsystems of tillage quality digital control in the same system and the automation of differentiated tillage.Производство почвообрабатывающей техники ориентировано на растущее применение почвозащитного и ресурсосберегающего земледелия и использование принципов точного сельского хозяйства в почвообработке. Возникло понятие дифференцированной обработки почвы, которая занимает промежуточное положение между традиционной и противоэрозийной (неглубокой) обработками. Провели анализ технических систем цифрового контроля качества обработки почвы с учетом указанных тенденций. Показали, что в научной литературе существует определенная несогласованность в наименованиях систем обработки почвы. (Цель исследования) Представить аналитический обзор технических систем цифрового контроля качества обработки почвы. (Материалы и методы) Использовали проспекты компаний-производителей почвообрабатывающей техники, патенты и научные работы. (Результаты и обсуждение) Рассмотрели существующие на мировом рынке коммерческие предложения в сфере дифференцированной обработки почвы и системы цифрового контроля качества. Представили анализ аналогичных решений, имеющихся в мировой научной литературе. Изучили вопросы контроля угла атаки дисков, глубины обработки почвы, гребнистости поверхности почвы, средних размеров комков почвы, количества пожнивных остатков, определения свойств почвы бесконтактным способом. Выявили разрозненность научных и производственных разработок в сфере контроля качества обработки почвы. Предложили объединить их в одну систему, чтобы автоматизировать процесс дифференцированной почвообработки. (Выводы) Показали, что почвообрабатывающая техника становится все более адаптируемой в плане выполнения специфических требований фермера к обработке почвы. Определили перспективные направления для будущего развития почвообрабатывающих машин: включение в одну систему разных подсистем цифрового контроля качества обработки почвы и автоматизация дифференцированной почвообработки

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Drones and Geographical Information Technologies in Agroecology and Organic Farming

Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license

Estimation of crop residue cover in high-resolution RGB images using features from a pre-trained convolutional neural network

Plant residue on the soil surface increases the sustainability food and fiber production in agricultural systems. Automated assessments of residue cover based on imagery has the potential to reduce labor and human bias associated with in-field measurements. Our objective was to evaluate the capacity of a transfer learning strategy to improve estimates of residue cover derived from high-resolution RGB images. The imagery for the project was collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. At each field location, 50 contiguous 0.3 m x 0.2 m region of interest (ROI) images (ground sampling distance of 0.014 cm pixel-1) were extracted from imagery resulting in a dataset of 4,400 ROI images; 3,000 used for cross validation and training (data collected in 2018) and 1,400 used for testing (data collected in 2019). The percent residue for each ROI image (ground truth) was determined by a bullseye grid method (n = 100). Features were extracted from ROI images using the VGGNet16 model, a convolutional neural network model. To reduce feature numbers, we averaged the features based on each kernel resulting 1,472 feature dataset per ROI. After the extraction, we compared three feature selection strategies: recursive feature elimination support vector machine classification (RFE-SVM), sequential forward feature selection classification (SFFS-SVM) and forward regression feature selection (FRFS). Best locations outcomes were obtained with RFE-SVM (r2 = 0.93, MAE = 4.9, with three outliers) and FRFS (r2 = 0.94, MAE = 5.2, with two outliers). The three models had no apparent pattern of correlation among selected features and limited overlap in outliers suggesting unique characteristics among the three selected feature sets. These results were superior to previous research based the same data set using 70 manually extracted known features. This suggested that transfer learning through features extracted from VGGNet16 pre-trained on ImageNet was a successful strategy for estimating residue cover. This research also confirmed the utility of high-resolution RGB imagery to quantify residue cover in agricultural systems.Includes bibliographical references

Leveraging very-high spatial resolution hyperspectral and thermal UAV imageries for characterizing diurnal indicators of grapevine physiology

Efficient and accurate methods to monitor crop physiological responses help growers better understand crop physiology and improve crop productivity. In recent years, developments in unmanned aerial vehicles (UAV) and sensor technology have enabled image acquisition at very-high spectral, spatial, and temporal resolutions. However, potential applications and limitations of very-high-resolution (VHR) hyperspectral and thermal UAV imaging for characterization of plant diurnal physiology remain largely unknown, due to issues related to shadow and canopy heterogeneity. In this study, we propose a canopy zone-weighting (CZW) method to leverage the potential of VHR (≤9 cm) hyperspectral and thermal UAV imageries in estimating physiological indicators, such as stomatal conductance (Gs) and steady-state fluorescence (Fs). Diurnal flights and concurrent in-situ measurements were conducted during grapevine growing seasons in 2017 and 2018 in a vineyard in Missouri, USA. We used neural net classifier and the Canny edge detection method to extract pure vine canopy from the hyperspectral and thermal images, respectively. Then, the vine canopy was segmented into three canopy zones (sunlit, nadir, and shaded) using K-means clustering based on the canopy shadow fraction and canopy temperature. Common reflectance-based spectral indices, sun-induced chlorophyll fluorescence (SIF), and simplified canopy water stress index (siCWSI) were computed as image retrievals. Using the coefficient of determination (R2) established between the image retrievals from three canopy zones and the in-situ measurements as a weight factor, weighted image retrievals were calculated and their correlation with in-situ measurements was explored. The results showed that the most frequent and the highest correlations were found for Gs and Fs, with CZW-based Photochemical reflectance index (PRI), SIF, and siCWSI (PRICZW, SIFCZW, and siCWSICZW), respectively. When all flights combined for the given field campaign date, PRICZW, SIFCZW, and siCWSICZW significantly improved the relationship with Gs and Fs. The proposed approach takes full advantage of VHR hyperspectral and thermal UAV imageries, and suggests that the CZW method is simple yet effective in estimating Gs and Fs

The Response of Soybean Yield to Different Cropping Pattern in a Long-term experiment on Chernozem

Considering the area and global production of the soybean, a relatively small number of papers address different aspects of its cultivation patterns and place in crop sequences. This leads to a lack of knowledge on the role and significance of soybean in different European cropping systems (CS). There is a consensus that soybean is favourable preceding crop and alongside soybean yield increases in crop rotations, however, the length and cropping patterns significantly affected the “rotation advantage” of soybean cropping systems. On the contrary, soybean monoculture has been widely used in practice despite potential adverse effects and higher risk of production. Therefore, the aim of this study was to assess yield differences of selected soybean cropping patterns in relation to the temperate climatic condition for 2008-2016 period. Analysed CS foreseen growing soybeans with maize (M), winter wheat (W) sugar beet (B) and soybean (S) as follows: (i) unfertilized 3-year rotation (MSW), (ii)3-year rotation (MSWf), (iii) 3-year rotation + cover crops (MSWccf), (iv) 4-year rotation (MSWBf), (v)monoculture SSSf and (vi) 3-year rotation with manure (MSWam). The trial was part of the long-term experiment“Plodoredi“ on the experimental station Rimski Šančevi of the Institute of Filed and Vegetable crops Novi Sadestablished on Haplic Chernozem. Regular tillage operations were used including mouldboard ploughing inautumn, compactor for levelling furrows in spring, multi-tiller for seedbed preparation and sowing in April. Inter-row cultivation and plant protection were done in May. Fertilization was not applied for soybean directly but forother crops in rotation with respect to soil chemical properties and anticipated yield. During 10 years period leadingsoybean varieties was grown with addition of biological fertilizer Nitragin. In average, a significantly higher yieldwas obtained at the 3-year fertilized rotation (3.25 t/ha) and the lowest at the monoculture (1.7 t/ha). Among theinvestigated years, a higher yield was obtained in 2013. and the lowest at 2017. A highly significant correlationwith soybean yield was found for rainfall (r=0.78** p<0.01) and a significant correlation for the temperature(r=0.74*, p<0.05) during the vegetation period (April-September). Climatic data evaluation reviled that asignificant effect on soybean yield for the temperature was found for August. For monthly sum rainfall, asignificant effect on yield was found for the June compared to other months. Our study demonstrates that soybeanin 3-year rotation benefited from crop sequence compared to monocropping. In addition, animal manure used formaize has not significantly affected soybean yield as well as growing soybean in a 4-year rotation. Maindisadvantages in monocropping are weed control and less efficient plant protection. Long-term unfertilizedsoybean demonstrated the adaptability of grown verities to low input systems and showed potential of sustainingyield in favourable years but the crop yield largely depends on the performance of winter wheat and maize

Conservation tillage systems could increase maize resilience to climate change

Climate changes severely affect agricultural production, particularly dry farming. Even crops that are relatively tolerant to drought, like maize, have been recently influenced by meteorological extremes, thus significantly reducing yield potential. The adjustment of cropping technology in which soil tillage system is an essential maize growing practice is the only way for stable maize cultivation. The objective of the study was to monitor and understand how different tillage systems and fertilizer rates influence grain yield of maize grown in dry farming conditions. The study was initiated in 1978 in Zemun Polje, Belgrade, Serbia, on the chernozem soil type, while the results from 2011-2021 period are analysed. Maize hybrid ZP SC 606 has been sown at the middle of April every year. The experiment was set as a split-split-plot block design with four replications. The main treatments were three tillage systems: NT - no-till, RT – reduced, and CT - conventional tillage. In the no-tillage treatment, maize seeds were sown in the upper soil layer of 5-7 cm, directly by a special planter. In the reduced tillage treatment, soil tillage was performed in the depth of 8-10 cm, with a rotovator in autumn, while sowing is conducted in the spring after seedbed preparation (10-12 cm) with a conventional drill. The conventional tillage treatment consisted in shallow ploughing, immediately after wheat harvest in the depth of 10-15 cm, primary tillage (ploughing) in the depth of 25-30 cm in autumn and seedbed preparation (10-12 cm) inspring. The fertilizer treatments, as subplots, included control (Ø) - without fertilization, incorporation of 50kg/ha N, 50 kg/ha P and 50 kg/ha K in the autumn and supplemental N addition up to the 180 kg/ha N (F1) and240 kg/ha N (F2) before sowing in the spring. Variations in meteorological conditions of the season caused highvariability in maize grain yield. The lowest grain yield, in average, was achieved in 2021 (3.38 t/ha) and thehighest in 2014 (11.33 t/ha). Among tillage practices, higher average yield was achieved with CT (9.38 t/ha)while lower values were in NT (6.14 t/ha). In dry seasons and seasons with extreme variations (2012, 2017 and2021), stable and even higher yields were achieved in RT and NT. Thus, in 2021 the highest yield was achievedin NT (to 2.34 t/ha concerning CT). Increased fertilizer rates resulted in yield increase, from 6.59 t/ha in Ø to8.35 t/ha in F2. The differences in grain yield between fertilizer rates were higher in RT. Correlation analysisindicated that with tillage intensification (CT), yield potential is highly negatively dependent on temperature,particularly during grain filing (correlation coefficient 0.8) and high and positive with precipitation amount(correlation coefficient > 0.7), while this dependence was reduced, especially in NT (correlation coefficients <0.5). Irrespective that CT contributed to the higher grain yield in average, less intensive tillage systems enabledyield stability in drier and extreme seasons. It was noticeable that increased fertilizer rates were required inreduced systems, such as NT and particularly RT