1,721,026 research outputs found
Investigating The Effects Of Soil Management In A Long Term Experiment In Southern Italy: Preliminary Results For The Assessment Of The Soil Hydraulic Properties Using The Beerkan Estimation Of Soil Transfer Parameter (BEST) Methodology
No full textIn the last few years, more farmers are moving towards a conservative agriculture based on a set of
practices such as zero tillage (or no-tillage), reduced tillage (such as minimum tillage), mulching and
cover crops, aimed to prevent soil degradation, to improve water conservation and to enhance soil
nitrogen through legumes in rural landascapes (Godfray et al., 2010). Soil hydraulic properties, i.e. the
hydraulic conductivity function (HCF) and the water retention curve (WRC), are widely used to quantify
the effect of soil management on soil physical and hydraulic properties and therefore, to evaluate the
sustainability of the cultivation systems regarding these aspects. In particular, soil system behaviour
should be monitored both during the transition period from conventional to conservative management
and in the long-term period, thus considering multiyear experiments and repeated measures in space and
time. However, soil hydraulic properties measurements are costly and time-consuming (AnguloJaramillo et al., 1997); consequently, simple, rapid, cheap and accurate methodologies should be applied,
such as the Beerkan Estimation of Soil Transfer parameters (BEST) procedure (Lassabatère et al., 2006),
that allows for the simultaneous determination of HCF and WRC. This paper presents the preliminary
results of a study aimed at investigating the short- and long-term impacts of soil management and crops
on soil physical and hydraulic properties. The dataset will be used also to investigate the spatial
variability of key soil variables in a typical Mediterrean agro-environment
Land Suitability Assessment For Linseed Introduction In Tuscany Based On A Cultivar-Specific Phenological Model
No full textCrop diversification is one of the main strategies of agroecological transition, playing a key role in enhancing land resource utilization, reducing the agricultural inputs, alleviating biotic and abiotic stresses, and stabilizing yields and economic returns. At the same time, it is a key adaptive action in response to weather challenges. In this contest, linseed represents a possible alternative for Tuscany cropping systems as autumn-winter crop thanks to its ability to withstand low winter temperatures a few degrees below zero, even in the early stages of plant vegetative development. However, crop performances, in term of seed yield and quality, can be negatively affected by spring frost during the flowering stages, as well as by high temperatures and drought occurring from the early flowering stage to seed development, with adverse effects on flower fertility and seed filling (Cross et al., 2003). On the other hand, Tuscany region is characterised by a Mediterranean climate with a great interannual variability in weather conditions with frequent occurrences of spring frost, that usually takes place during April, and high temperatures and prolonged drought in the late spring. Furthermore, rainfall distribution and crop evapotranspiration are strongly affected by the orography of the region, as well as minimum and maximum temperatures are influenced not only by latitude, but also by altitude and distance from the sea. Finally, the different soil texture gradients determine, under the same weather conditions, a strong variability in crop responses due to their effect in the soil hydrological properties and, consequently, on the entire soil-water-crop system. So, within the "SIC-OLEAT - Crop Innovation Systems for Tuscany Oilseed Crops" project, funded by Tuscany Region (PSR 2014-2020), land suitability to linseed cultivation in Tuscany was evaluated, starting from the phenological observations directly recorded from the experimental trials carried out in 2 different locations (San Piero a Grado – Pisa province and Alberese – Grosseto province) for 2 consecutive growing seasons (2019 and 2020) comparing 5 linseed varieties and different sowing times
Soil tillage and fertilization affect durum wheat and weeds interactions in Mediterranean environment
No full textNo abstract availabl
Linseed As Opportunity For Increasing Cropping System Diversification and Resilience: The Sic-Oleat Project Experience
No full textThe use of suitable crop species and varieties and the diversification of cropping systems are key adaptive actions in response to weather challenges (Hufnagel et al., 2020). In this context, minor oilseed crops have been making their way into our diets and production systems, representing a reasonable choice in helping farmers to increase the efficiency of their food systems and to cope with challenging climate adversities and economic risks. The SIC-OLEAT project aimed to assess the adaptability of linseed (Linum usitatissimum L.), as source of vegetable oil and protein, to the pedo-climatic conditions of Tuscany Region (Central Italy), with a site-specific approach. Consequently, a 2-years field trial has been carried out in two constrasting environments, representative of the northern and southern coastal areas, respectively, comparing five commercial linseed varieties with the aim to hypothesize production path for the inclusion of this new crop within traditional rainfed cereal‐based cropping systems
Hyperspectral Vegetation Indices To Assess Water And Nitrogen Status Of Sweet Maize Crop_SIA
No full textWater and nitrogen (N) have long been known as two primary restricting inputs for crop production.
Matching N supply to water availability is essential to accomplish an optimal crop response and
satisfactory use efficiency levels for those input resources. Proximal sensing methods enable rapid, non destructive water and nutrient deficiency determination, and they are widely used in the precision
agriculture (Pinter et al., 2003). Narrow-band vegetation indices use reflectance in red and near-infrared
to collect the red-edge section of the spectrum, thus they have been favourably included in studies aiming
to estimate crop nitrogen concentration (Chen et al., 2010), leaf chlorophyll content (Vincini et al., 2011),
light-use efficiency (Garbulsky et al., 2010), as well as to detect water stress (Zarco-Tejada et al., 2013)
and diseases (Calderón et al., 2013).
In this study, the sensitivity of narrow-band vegetation indices to describe the response of sweet maize
under different water and nitrogen management approaches was investigated. To this aim selected
structural, red-edge and water-band indices were chosen, and their performance was evaluated
Preliminary Results on the Use of Dark Green Colour Index (DGCI) to Evaluate the Nutritional Status of Camelina Sativa (L.) Crantz
No full textNowadays, the use of digital radiometric sensors in agriculture is a booming sector. Their ability to acquire quantitative and qualitative data on crops, combined with their ever-increasing versatility, which has made it possible to upload them onto UAV (Unmanned Aerial Vehicle), has made these technologies increasingly used within the most innovative agricultural realities.
The most widely used sensors in the field of Precision Agriculture (PA) are certainly the multi- and hyper-spectral, which, being able to acquire data even outside the visible spectrum, allow the algebraic combination of characteristic spectral bands for the definition of the Vegetation Indices (VIs), useful for highlighting some specific properties of the vegetation (biomass of the canopy, absorbed radiation, chlorophyll content, etc.). The Normalized Difference Vegetation Index (NDVI), in this sense, appears to be one of the most used VIs for monitoring the nutritional status of the crop, as it has proven to be very reliable and acquirable in a quickly way.
On the other hand, to calculate this index, it is necessary to use a multispectral camera, which, in addition to its high cost, requires a certain specific know-how both in the acquisition and processing phases, which often make these instruments not very accessible to small and medium-sized farms.
Some recent studies, however, have identified a possible low-cost alternative (1, 2) which involves the use of Red Green and Blue (RGB) images, that can be acquired by any normal camera. This technique is based on the conversion of the RGB values of each pixel into the Hue, Saturation and Brightness (HSB) values, in order to improve the quantification of the green of the image (2), to then calculate a new VIs, the Dark Green Colour Index (DGCI). Nevertheless, if this index is widely used on the turfgrass, its application on field crops is being explored on a limited number of crops. For this reason, this work aims to evaluate the correlation of this index with the NDVI on a crop of Camelina Sativa (L.) Crantz. Specifically, the values of the two indices acquired via UAV will be compared to be able to evaluate the two techniques already at an operational level
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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