Gestione delle infestanti del colza (<i>Brassica napus</i> var. <i>oleifera</i>) in ambiente mediterraneo: risultati preliminari

La coltura del colza è caratterizzata da un lento accrescimento nelle prime fasi di sviluppo, fasi in cui la rapida crescita delle infestanti risulta potenzialmente limitante. Una efficace competizione della coltura verso le infestanti si verifica solo dopo che la copertura vegetale ricopre completamente le interfile. Inoltre, le infestanti dotate di ramificazioni e di apparato radicale vigoroso esercitano una severa competizione per gli elementi nutritivi disponibili (Bishnoi et al., 2007). In Brassica sp.pl., Bishnoi et al. (2007) hanno riportato perdite comprese tra il 30 e il 50%, a seconda dell’accrescimento e della persistenza delle infestanti nel campo. Le difficoltà nella gestione delle infestanti del colza, oltre che derivare dalla possibile inadeguata preparazione del letto di semina e da un eventuale insufficiente grado di umidità del suolo, risiede nelle ridotte dimensioni dei semi e nel loro breve tempo di germinazione, che rendono la coltura molto sensibile alle avversità ambientali nelle sue fasi iniziali (Paudel et al., 2008). In mancanza di validi prodotti ad ampio spettro dicotiledonicida applicabili nella post-emergenza della coltura, il diserbo del colza si basa prevalentemente sull’impiego del metazachlor, che può essere applicato anche nei primi stadi di sviluppo. Rimane quasi insoluto il problema del controllo delle crucifere quali Sinapis, Rapistrum, Raphanus, e Brassica, specie tardive generalmente presenti in autunno e durante gli inverni miti. Obiettivo dello studio è stato quello di valutare l’effetto di diversi trattamenti di controllo delle infestanti, basati sull’impiego di metazachlor, sulla resa e sue componenti in colza var. Kabel

LCA study of oleaginous bioenergy chains in a Mediterranean environment

This paper reports outcomes of life cycle assessments (LCAs) of three different oleaginous bioenergy chains (oilseed rape, Ethiopian mustard and cardoon) under Southern Europe conditions. Accurate data on field practices previously collected during a three-year study at two sites were used. The vegetable oil produced by oleaginous seeds was used for power generation in medium-speed diesel engines while the crop residues were used in steam power plants. For each bioenergy chain, the environmental impact related to cultivation, transportation of agricultural products and industrial conversion for power generation was evaluated by calculating cumulative energy demand, acidification potential and global warming potential. For all three bioenergy chains, the results of the LCA study show a considerable saving of primary energy (from 70 to 86 GJ·ha−1) and greenhouse gas emissions (from 4.1 to 5.2 t CO2·ha−1) in comparison to power generation from fossil fuels, although the acidification potential of these bioenergy chains may be twice that of conventional power generation. In addition, the study highlights that land use changes due to the cultivation of the abovementioned crops reduce soil organic content and therefore worsen and increase greenhouse gas emissions for all three bioenergy chains. The study also demonstrates that the exploitation of crop residues for energy production greatly contributes to managing environmental impact of the three bioenergy chains

Validation of Rapid and Low-Cost Approach for the Delineation of Zone Management Based on Machine Learning Algorithms

Proximal soil sensors are receiving strong attention from several disciplinary fields, and this has led to a rise in their availability in the market in the last two decades. The aim of this work was to validate agronomically a zone management delineation procedure from electromagnetic induction (EMI) maps applied to two different rainfed durum wheat fields. The k-means algorithm was applied based on the gap statistic index for the identification of the optimal number of management zones and their positions. Traditional statistical analysis was performed to detect significant differences in soil characteristics and crop response of each management zones. The procedure showed the presence of two management zones at both two sites under analysis, and it was agronomically validated by the significant difference in soil texture (+24.17%), bulk density (+6.46%), organic matter (+39.29%), organic carbon (+39.4%), total carbonates (+25.34%), total nitrogen (+30.14%), protein (+1.50%) and yield data (+1.07 t ha−1). Moreover, six unmanned aerial vehicle (UAV) flight missions were performed to investigate the relationship between five vegetation indexes and the EMI maps. The results suggest performing the multispectral images acquisition during the flowering phenological stages to attribute the crop spatial variability to different soil proprieties

Wild asparagus domestication for food/energy cropping system setup

The solar greenhouse sector is currently unbalanced towards energy production. Thus, the introduction of new crop options, such as wild asparagus, could contribute to the promotion of economic and environmental sustainability in these food/energy systems (mixed-systems). We hypothesized that wild asparagus is able to adapt both to sunny and partially shaded environments provided that both nutrient and water supply are guaranteed. Over a three-year experiment, we carried out an intensive examination of within-season phenological, physiological and productive dynamics under a greenhouse with 50% of the roof area covered with photovoltaic panels. Under the photovoltaic roof the net assimilation rate was on average 5 time lower, averaged over the growing seasons (0.6 μmol CO2 m-2 s-1), resulting in negative results for some monitoring dates. However, lower net assimilation rate did not negatively impact spears production in terms of number, length and diameter. The year of establishment affected the length of the spear, which was 4 cm shorter in 2013 than in 2014 and 2015, when no significant difference was observed. The novelty proposed in this study could be a successful option for farmers to promote production diversification and a promising strategy to guarantee the environmental and economic sustainability of the whole mixed system

Genomic analysis of Sardinian 26544/OG10 isolate of African swine fever virus

Abstract Comparative genomic analysis aims to underscore genetic assortment diversification in distinct viral isolates, to identify deletions and to carry out evolutionary studies. We sequenced the first complete genome of an ASFV p72 genotype I strain isolated from domestic pigs in Sardinia (Italy) using Next-Generation Sequence (NGS) technology. The genome is 182,906 bp long, contains 164 ORFs and has a 99.20% nucleotide identity to the L60 strain. Comparison analysis against the 16 ASFV genomes available in the database showed that 136 ORFs are present in nine ASFV isolates annotated to date. The most divergent ORFs codify for uncharacterized proteins such as X69R and DP96R, which have 51.3% and 70.4% nucleotide identity to the other isolates. A comparison between the Sardinian isolate and the avirulent isolates OURT 88/3, NHV, BA71V was also carried out. Major variations were found within the multigene families (MGFs) located in the left and right genome regions

Integration of oil-seed crops in Mediterranean agro-pastoral systems to supply bio-fuels to local power industry

The growing of rape seed (Brassica napus var. oleifera D.C.) and Ethiopian mustard (Brassica carinata A. Braun) as oilseed crop for biodiesel production in southern Europe has gained new interest, following the implementation of policies aimed at increasing the production of locally produced biofuels. The study reported in this paper is part of a feasibility study designed to provide a scientific assessment on the introduction of oil seed crops in the context of the Mediterranean agro-pastoral systems of central Sardinia. Locally, the oil seed demand is from a 34 MW electric power station recently installed by Biopower Sardegna Spa, who funded the study, to supply the local industrial site. The overall objective of the experiments is also to build a dataset to adapt CROPGRO model of DSSAT (Jones et al., 2003) to rapeseed. In this paper, we will provide preliminary data from the field experiments on rapeseed and an overview of the research design

Fertilization and soil management machine learning based sustainable agronomic prescriptions for durum wheat in Italy

The agricultural sector is challenged to produce more food with less available land. This challenge is compounded by increasing production input costs, which are further impacted by geopolitical changes and the adverse effects of climate change. Finding sustainable solutions to address these challenges is crucial. Machine learning can be a valuable tool in agriculture to optimize soil, nutrient, and crop management, helping to maximize food production on limited land and mitigate the effects of climate change. Machine learning models hold great promise in agriculture; however, their success is contingent on ease of use, accessibility for farmers, and the perceived utility of such models by the agricultural community. This work aimed to develop a meta-machine learning that allows the simulation of different combinations of soil and nitrogen management of durum wheat in Italy. This model aimed to assist decision makers and stakeholders in determining the most effective agronomic management based on attainable crop yield and potential income margins derived from the use of agronomic inputs. The meta-machine learning model was developed and tested across four sites located in the Italian regions of Marche and Basilicata. These sites featured different experimental designs, and durum wheat was grown for several years. The study involved the comparison of a total of eleven different nitrogen levels. The meta-machine learning was composed by linked classification and regression machine learning models. These components were trained using a multi-data source approach, which included data from remote sensing, crop phenology, soil chemical properties, weather data, soil management, and nitrogen levels, to predict durum wheat yield. The classification task employed a Random Forest model with an accuracy of 0.98, kappa of 0.96 and recall of 0.98 for predicting the crop phenology while the yield prediction task was performed by a Neural Network model with an R squared of 0.90, Root Mean Square Error of 0.59, Mean Absolute Error of 0.45 and Mean Absolute Percentage Error of 0.17. The variable importance analysis was conducted to identify the most important covariates that allow to improve the model’s accuracy. This analysis revealed that temperature, precipitation, NDVI (Normalized Difference Vegetation Index), and nitrogen input are the most important factors. The meta-model was used to run simulations of 30 different combinations of soil management and fertilization levels. These simulations aimed to identify the most effective agronomic strategy for each of the farm sites. The no tillage management have been found to result in increased grain yield. The Marginal Fertilizer Yield Index was used to determine the optimal nitrogen application for the crop. The potential transferability to field conditions of the model is facilitated by its utilization of publicly available spatial datasets, which can enable the broader application of the meta-model

Increasing the agricultural sustainability of closed agrivoltaic systems with the integration of vertical farming: A case study on baby-leaf lettuce

The photovoltaic (PV) greenhouses are closed agrivoltaic (CA) systems that allow the production of energy and food on the same land, but may result in a yield reduction when the shading of the PV panels is excessive. Adopting innovative cropping systems can increase the yield of the CA area, generating a more productive and sustainable agrosystem. In this case study we quantified the increase of land productivity derived from the integration of an experimental vertical farm (VF) for baby leaf lettuce inside a pre-existing commercial CA. The mixed system increased the yield by 13 times compared to the CA and the average LER was 1.31, but only 12 % of the energy consumption was covered by the CA energy. To achieve the energy self-sufficiency and avoid the related CO2 emissions, the VF area should not exceed 7–18 % of the CA area, depending on the PV energy yield and the daily light integral (DLI) of the LED lighting, meaning a land consumption from 5 to 14 times higher than the VF area. The support of the PV energy was essential for the profitability of the VFCA. Design features and solutions were proposed to increase the agronomic and economic sustainability of the VFCA. The VFs can be considered a possible answer for the reconversion of the actual underutilized CAs with high PV cover ratios into productive and efficient cropping systems, but a trade-off between energy production and land consumption should be identified to ensure an acceptable environmental sustainability of the mixed system

Examining wheat yield sensitivity to temperature and precipitation changes for a large ensemble of crop models using impact response surfaces

Impact response surfaces (IRSs) depict the response of an impact variable to changes in two explanatory variables as a plotted surface. Here, IRSs of spring and winter wheat yields were constructed from a 25-member ensemble of process-based crop simulation models. Twenty-one models were calibrated by different groups using a common set of calibration data, with calibrations applied independently to the same models in three cases. The sensitivity of modelled yield to changes in temperature and precipitation was tested by systematically modifying values of 1981-2010 baseline weather data to span the range of 19 changes projected for the late 21st century at three locations in Europe