Food Waste Causes in Fruit and Vegetables Supply Chains

Fruit and vegetables are a core component of healthy diets, but horticultural production and distribution activities suffer from a high incidence of surplus food and food waste. The intrinsic perishability of products as well recurring pests, diseases and contamination events are since long recognized to be primary reasons for fruit and vegetables wastage, but a more thorough knowledge of causes, including external events and internal strategies and practices, is necessary to design and implement waste reduction strategies. However, literature on waste causes in fruit and vegetables supply chains is rather fragmented. Most existing studies focus on single products, single deterioration mechanisms or single reuse or recycling choices, and hardly ever investigate more than one stage of the fruit and vegetables supply chain. The main objective of the paper is to offer an instrument for identifying in a comprehensive way the possible origin points and root issues behind food waste generation in the stages of fruit and vegetables supply chains. The research is conducted through the application of two methods. A first phase consists in a deep literature review, whose results are summarized in the so-called Causes Framework. This qualitative instrument shows the possible sources of fruit and vegetables surplus and waste, highlighting for each supply chain stage the high-priority causes and for each cause the fundamental root issue. The second research phase is a case study that shows how the Framework can be applied to pinpoint the most significant causes for specific supply chains. The unit of analysis is the supply chain of an Italian PGI pear. Primary information is gathered from 6 enterprises through 7 semi-structured interviews. The most critical causes of surplus and waste generation in the focal supply chain are found as the intersection between interview answers and Framework predictions. The paper integrates sparse pieces of knowledge on the processes of food waste generation in fruit and vegetables supply chains, and offers an instrument that may support private and public decision-makers in the reduction of horticultural waste

A sound understanding of a cropping system model with the global sensitivity analysis

The capability of cropping system models of depicting the crop and soil-related processes implies a high number of parameters. The aim of this work was to detect the key parameters, and the associated processes, of the ARMOSA cropping system model, considering two target outputs, crop yield and nitrogen leaching. A global sensitivity analysis (SA) was carried out in two steps: (1) the Morris method considering the whole set of parameters; (2) Sobol analysis was applied to the Morris outcome. The simulation was run on winter wheat in four soil types in Marchfeld (Austria, 2010–2018). Parameters affecting crop yield was the critical nitrogen concentration, the potential CO2 assimilation rate, and the drought sensitivity parameter. Nitrogen leaching was mainly affected by the decomposition of litter and the early aboveground biomass growth. The parameters ranking did not appreciably change across soil types. This study offers a quick and replicable methodology for model calibration

Soil organic carbon under conservation agriculture in Mediterranean and humid subtropical climates: Global meta‐analysis

Conservation agriculture (CA) is an agronomic system based on minimum soil disturbance (no-tillage, NT), permanent soil cover, and species diversification. The effects of NT on soil organic carbon (SOC) changes have been widely studied, showing somewhat inconsistent conclusions, especially in relation to the Mediterranean and humid subtropical climates. These areas are highly vulnerable and predicted climate change is expected to accentuate desertification and, for these reasons, there is a need for clear agricultural guidelines to preserve or increment SOC. We quantitively summarized the results of 47 studies all around the world in these climates investigating the sources of variation in SOC responses to CA, such as soil characteristics, agricultural management, climate, and geography. Within the climatic area considered, the overall effect of CA on SOC accumulation in the plough layer (0–0.3 m) was 12% greater in comparison to conventional agriculture. On average, this result corresponds to a carbon increase of 0.48 Mg C ha−1 year−1. However, the effect was variable depending on the SOC content under conventional agriculture: it was 20% in soils which had ≤ 40 Mg C ha−1, while it was only 7% in soils that had > 40 Mg C ha−1. We proved that 10 years of CA impact the most on soil with SOC ≤ 40 Mg C ha−1. For soils with less than 40 Mg C ha−1, increasing the proportion of crops with bigger residue biomasses in a CA rotation was a solution to increase SOC. The effect of CA on SOC depended on clay content only in soils with more than 40 Mg C ha−1 and become null with a SOC/clay index of 3.2. Annual rainfall (that ranged between 331–1850 mm y−1) and geography had specific effects on SOC depending on its content under conventional agriculture. In conclusion, SOC increments due to CA application can be achieved especially in agricultural soils with less than 40 Mg C ha−1 and located in the middle latitudes or in the dry conditions of Mediterranean and humid subtropical climates

Biomass production and energy balance of herbaceous and woody crops on marginal soils in the Po valley

A wealth of data and information on the cultivation of perennial biomass crops has been collected, but direct comparisons between herbaceous and woody crops are rare. The main objective of this research was to compare the biomass yield, the energy balance and the biomass quality of six perennial bioenergy crops: Populus spp., Robinia pseudoacacia, Salix spp., Arundo donax, Miscanthus 9 giganteus, and Panicum virgatum, grown in two marginal environments. For giant reed and switchgrass, two levels of nitrogen fertilization were applied annually (0–100 kg ha 1). Nitrogen fertilization did not affect biomass or energy production of giant reed; thus, it significantly reduced the energy return on investment (EROI) (from 73 to 27). In switchgrass, nitrogen fertiliza- tion significantly increased biomass production and the capacity of this crop to respond to water availability, making it a favorable option when only biomass production is a target. Net energy gain (NEG) was higher for herbaceous crops than for woody crops. In Casale, EROI calculated for poplar and willow (7, on average) was significantly lower than that of the other crops (14, on average). In Gariga, the highest EROI was calculated for miscanthus (98), followed by nonfertilized giant reed and switchgrass (82 and 73, respectively). Growing degree days10 during the cropping season had no effect on biomass production in any of the studied species, although water availability from May to August was a major factor affecting biomass yield in herbaceous crops. Overall, herbaceous crops had the highest ranking for bioenergy production due to their high biomass yield, high net energy gain (NEG), and biomass quality that renders them suitable to both biochemical and thermochemical conversion. Miscanthus in particular had the highest EROI in both locations (16 and 98, in Casale and Gariga), while giant reed had the highest NEG on the silty-loam soil of Gariga

Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data

[EN] The agricultural sector faces the challenge of ensuring food security without an excessive burden on the environment. Simulationmodels provide excellent instruments for researchers to gainmore insight into relevant processes and best agricultural practices and provide tools for planners for decision making support. The extent to which models are capable of reliable extrapolation and prediction is important for exploring new farming systems or assessing the impacts of future land and climate changes. A performance assessmentwas conducted by testing six detailed state-of-the-artmodels for simulation of nitrate leaching (ARMOSA, COUPMODEL, DAISY, EPIC, SIMWASER/STOTRASIM, SWAP/ANIMO) for lysimeter data of the Wagna experimental field station in Eastern Austria, where the soil is highly vulnerable to nitrate leaching. Three consecutive phases were distinguished to gain insight in the predictive power of themodels: 1) a blind test for 2005 2008 in which only soil hydraulic characteristics, meteorological data and information about the agricultural management were accessible; 2) a calibration for the same period in which essential information on field observations was additionally available to the modellers; and 3) a validation for 2009 2011 with the corresponding type of data available as for the blind test. A set of statistical metrics (mean absolute error, root mean squared error, index of agreement,model efficiency, root relative squared error, Pearson's linear correlation coefficient) was applied for testing the results and comparing the models. None of the models performed good for all of the statistical metrics. Models designed for nitrate leaching in high-input farming systems had difficulties in accurately predicting leaching in low-input farming systems that are strongly influenced by the retention of nitrogen in catch crops and nitrogen fixation by legumes. An accurate calibration does not guarantee a good predictive power of the model. Nevertheless all models were able to identify years and crops with high- and low-leaching rates.This research was made possible by the GENESIS project of the EU 7th Framework Programme (Project No. 226536; FP7-ENV-2008-1). We are grateful for the experimental data provided by Joanneum Raum (Graz, Austria). The modelling team of Democritus University of Thrace would like to thank Per-Erik Jansson (Royal Institute of Technology, Stockholm, Sweden) for his valuable help during the application of Coup Model.Groenendijk, P.; Heinen, M.; Klammler, G.; Fank, J.; Kupfersberger, H.; Pisinaras, V.; Gemitzi, A.... (2014). Performance assessment of nitrate leaching models for highly vulnerable soils used in low-input farming based on lysimeter data. Science of the Total Environment. 499:463-480. https://doi.org/10.1016/j.scitotenv.2014.07.002S46348049

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

The LANDSUPPORT geospatial decision support system (S-DSS) vision: Operational tools to implement sustainability policies in land planning and management

Nowadays, there is contrasting evidence between the ongoing continuing and widespread environmental degradation and the many means to implement environmental sustainability actions starting from good policies (e.g. EU New Green Deal, CAP), powerful technologies (e.g. new satellites, drones, IoT sensors), large databases and large stakeholder engagement (e.g. EIP-AGRI, living labs). Here, we argue that to tackle the above contrasting issues dealing with land degradation, it is very much required to develop and use friendly and freely available web-based operational tools to support both the implementation of environmental and agriculture policies and enable to take positive environmental sustainability actions by all stakeholders. Our solution is the S-DSS LANDSUPPORT platform, consisting of a free web-based smart Geospatial CyberInfrastructure containing 15 macro-tools (and more than 100 elementary tools), co-designed with different types of stakeholders and their different needs, dealing with sustainability in agriculture, forestry and spatial planning. LANDSUPPORT condenses many features into one system, the main ones of which were (i) Web-GIS facilities, connection with (ii) satellite data, (iii) Earth Critical Zone data and (iv) climate datasets including climate change and weather forecast data, (v) data cube technology enabling us to read/write when dealing with very large datasets (e.g. daily climatic data obtained in real time for any region in Europe), (vi) a large set of static and dynamic modelling engines (e.g. crop growth, water balance, rural integrity, etc.) allowing uncertainty analysis and what if modelling and (vii) HPC (both CPU and GPU) to run simulation modelling 'on-the-fly' in real time. Two case studies (a third case is reported in the Supplementary materials), with their results and stats, covering different regions and spatial extents and using three distinct operational tools all connected to lower land degradation processes (Crop growth, Machine Learning Forest Simulator and GeOC), are featured in this paper to highlight the platform's functioning. Landsupport is used by a large community of stakeholders and will remain operational, open and free long after the project ends. This position is rooted in the evidence showing that we need to leave these tools as open as possible and engage as much as possible with a large community of users to protect soils and land

Wheat yield sensitivity to climate change across a European transect for a large ensemble of crop models

The sensitivity of a 26 member ensemble of process based wheat simulation models to perturbations in baseline temperature and precipitation was examined to construct impact response surfaces (IRS) of simulated yields