Joint measurements of NDVI and crop production data-set related to combination of management zones delineation and nitrogen fertilisation levels.

This research compared four nitrogen (N) management strategies (uniform N rate: UR, variable N rate based on crop proximal sensing: VR-PS, variable N rate based on management zones: VR-MZ and variable N rate based on integrating crop sensing and MZ: VR-PSMZ), evaluating their effect on maize grain yield, partial factor productivity (PFPN), and net return above N fertiliser cost (RANC). The study provided a practical tool for choosing the fertilisation strategy that best performs in each agro-environment. These datasets are a supplementary material to the research paper by [3]. Data were collected over seven site-years experiments conducted in North-Eastern Colorado (USA). In dataset 1, for each site-year, data includes geo-referred points where grain yield and Normalised Difference Vegetation Index (NDVI) were measured, each one associated with its respective N rate, management zone (MZ), PFPN, RANC, and N management strategy. In order to group the observations reflecting homogeneous crop vigour, NDVI values were clustered within NDVI classes. In dataset 2, the main soil properties measured in several geo-referred points in each location are provided.

Conversion from mineral fertilisation to MSW compost use: Nitrogen fertiliser value in continuous maize and test on crop rotation.

The recycling of agricultural wastes, co-products, and by-products is necessary for creating circular economic (closed loop) agro-food chains and more sustainable agro-ecosystems. The substitution of N mineral fertilisers with recycled organic fertiliser promotes a circular economy, makes the agricultural system more environmentally sustainable, and guarantees food security. Results from a continuous maize experiment and four-year rotation cropping systems (maize, winter wheat, maize, and soybean) were used in a three-year study that replaced part or all mineral fertilisers with Municipal Solid Waste Compost (MSWC). In the first experiment, two different fertilisation strategies, MSWC only (M-Com) and mineral fertilisers (M-Min), were compared with zero nutrients (M-Test 0), whereas in the rotation cropping systems, mineral fertilisation (R-Min) was compared with a combination of MSWC and mineral fertilisers (R-Com + Min). Depressed yields resulted in the initial year of compost application, but by the middle term (three years), MSWC fertilisation showed a good N fertiliser value, mainly for yield summer crops and integrated with N mineral fertilisers. Different soil indicators and the N content in crop tissues and soil suggested that the scarce N availability recorded mainly during the first year is responsible for yield reduction. Due to limited supplies of MSWC, soil total N and the stable organic fraction bound tightly to minerals (MOM), did not vary significantly in the three-year experiment. Conversely, the more labile organic fraction (fPOM) increased only in the top soil layers (0-15 cm). Also in the top layer, M-Com increased the amount of organic fraction occluded into soil aggregates (oPOM). Furthermore, replacement of N mineral fertiliser with compost effectively mitigated N2O emissions in wheat and maize. Overall, the fertiliser value of MSWC was maximised when it was used repeatedly and in combination with mineral fertiliser, especially in spring and summer crops.

Spatial management strategies for nitrogen in maize production based on soil and crop data.

Nitrogen (N) fertilisation determines maize grain yield (MGY). Precision agriculture (PA) allows matching crop N requirements in both space and time. Two approaches have been suggested for precision N management, i.e. management zones (MZ) delineation and crop remote and proximal sensing (PS). Several studies have demonstrated separately the advantages of these approaches for precision N application. This study evaluated their convenient integration, considering the influence of different PA techniques on MGY, N use efficiency (NUE), and farmer's net return, then providing a practical tool for choosing the fertilisation strategy that best applies in each agro-environment. A multi-site-year experiment was conducted between 2014 and 2016 in Colorado, USA. The trial compared four N management practices: uniform N rate, variable N rate based on MZ (VR-MZ), variable N rate based on PS (VR-PS), and variable N rate based on both PS and MZ (VR-PSMZ), based on their effect on MGY, partial factor productivity (PFPN), and net return above N fertiliser cost (RANC). Maize grain yield and PFPN maximisation conflicted in several situations. Hence, a compromise between obtaining high yield and increasing NUE is needed to enhance the overall sustainability of maize cropping systems. Maximisation of RANC allowed defining the best N fertilisation practice in terms of profitability. The spatial range in MGY is a practical tool for identifying the best N management practice. Uniform N supply was suitable where no spatial pattern was detected. If a high spatial range (>100 m) existed, VR-MZ was the best approach. Conversely, VR-PS performed better when a shorter spatial range (<16 m) was detected, and when maximum variability in crop vigour was observed across the field (range of variation = 0.597) leading to a larger difference in MGY (range of variation = 13.9 Mg ha-1). Results indicated that VR-PSMZ can further improve maize fertilisation for intermediate spatial structures (43 m).

Assessment of Methane and Nitrous Oxide Fluxes from Paddy Field by Means of Static Closed Chambers Maintaining Plants Within Headspace.

This protocol describes the measurement of greenhouse gas (GHG) emissions from paddy soils using the static closed chamber technique. This method is based on the diffusion theory. A known volume of air overlaying a defined soil area is enclosed within a parallelepiped cover (named "chamber"), for a defined period of time. During this enclosure period, gases (methane (CH4) and nitrous oxide (N2O)) move from soil pore air near their microbial source (i.e., methanogens, nitrifiers, denitrifiers) to the chamber headspace, following a natural concentration gradient. Fluxes are then estimated from chamber headspace concentration variations sampled at regular intervals throughout the enclosure and then analyzed with gas chromatography. Among the techniques available for GHG measurement, the static closed chamber method is suitable for plot experiments, as it does not require large homogenously treated soil areas. Furthermore, it is manageable with limited resources and can identify relationships among ecosystem properties, processes, and fluxes, especially when combined with GHG driving force measurements. Nevertheless, with respect to the micrometeorological method, it causes a minimal but still unavoidable soil disturbance, and allows a minor temporal resolution. Several phases are key to the method implementation: i) chamber design and deployment, ii) sample handling and analyses, and iii) flux estimation. Technique implementation success in paddy fields demands adjustments for field flooding during much of the cropping cycle, and for rice plant maintenance within the chamber headspace during measurements. Therefore, the additional elements to be considered with respect to the usual application of non-flooded agricultural soils consist of devices for: i) avoiding any unintended water disturbance that could overestimate fluxes, and ii) including rice plants within the chamber headspace to fully consider gases emitted through aerenchyma transportation.

Factors influencing stomatal conductance in response to water availability in grapevine: a meta-analysis.

The main factors regulating grapevine response to decreasing water availability were assessed under statistical support using published data related to leaf water relations in an extensive range of scion and rootstock genotypes. Matching leaf water potential (Ψleaf ) and stomatal conductance (gs ) data were collected from peer-reviewed literature with associated information. The resulting database contained 718 data points from 26 different Vitis vinifera varieties investigated as scions, 15 non-V. vinifera rootstock genotypes and 11 own-rooted V. vinifera varieties. Linearised data were analysed using the univariate general linear model (GLM) with factorial design including biological (scion and rootstock genotypes), methodological and environmental (soil) fixed factors. The first GLM performed on the whole database explained 82.4% of the variability in data distribution having the rootstock genotype the greatest contribution to variability (19.1%) followed by the scion genotype (16.2%). A classification of scions and rootstocks according to their mean predicted gs in response to moderate water stress was generated. This model also revealed that gs data obtained using a porometer were in average 2.1 times higher than using an infra-red gas analyser. The effect of soil water-holding properties was evaluated in a second analysis on a restricted database and showed a scion-dependant effect, which was dominant over rootstock effect, in predicting gs values. Overall the results suggest that a continuum exists in the range of stomatal sensitivities to water stress in V. vinifera, rather than an isohydric-anisohydric dichotomy, that is further enriched by the diversity of scion-rootstock combinations and their interaction with different soils.

Acquisition of Flavescence Dorée Phytoplasma by Scaphoideus titanus Ball from Different Grapevine Varieties.

Flavescence dorée (FD) is a threat for wine production in the vineyard landscape of Piemonte, Langhe-Roero and Monferrato, Italy. Spread of the disease is dependent on complex interactions between insect, plant and phytoplasma. In the Piemonte region, wine production is based on local cultivars. The role of six local grapevine varieties as a source of inoculum for the vector Scaphoideus titanus was investigated. FD phytoplasma (FDP) load was compared among red and white varieties with different susceptibility to FD. Laboratory-reared healthy S. titanus nymphs were caged for acquisition on infected plants to measure phytoplasma acquisition efficiency following feeding on different cultivars. FDP load for Arneis was significantly lower than for other varieties. Acquisition efficiency depended on grapevine variety and on FDP load in the source plants, and there was a positive interaction for acquisition between variety and phytoplasma load. S. titanus acquired FDP with high efficiency from the most susceptible varieties, suggesting that disease diffusion correlates more with vector acquisition efficiency than with FDP load in source grapevines. In conclusion, although acquisition efficiency depends on grapevine variety and on FDP load in the plant, even varieties supporting low FDP multiplication can be highly susceptible and good sources for vector infection, while poorly susceptible varieties may host high phytoplasma loads.

Assessing agro-environmental performance of dairy farms in northwest Italy based on aggregated results from indicators.

Dairy farms control an important share of the agricultural area of Northern Italy. Zero grazing, large maize-cropped areas, high stocking densities, and high milk production make them intensive and prone to impact the environment. Currently, few published studies have proposed indicator sets able to describe the entire dairy farm system and their internal components. This work had four aims: i) to propose a list of agro-environmental indicators to assess dairy farms; ii) to understand which indicators classify farms best; iii) to evaluate the dairy farms based on the proposed indicator list; iv) to link farmer decisions to the consequent environmental pressures. Forty agro-environmental indicators selected for this study are described. Northern Italy dairy systems were analysed considering both farmer decision indicators (farm management) and the resulting pressure indicators that demonstrate environmental stress on the entire farming system, and its components: cropping system, livestock system, and milk production. The correlations among single indicators identified redundant indicators. Principal Components Analysis distinguished which indicators provided meaningful information about each pressure indicator group. Analysis of the communalities and the correlations among indicators identified those that best represented farm variability: Farm Gate N Balance, Greenhouse Gas Emission, and Net Energy of the farm system; Net Energy and Gross P Balance of the cropping system component; Energy Use Efficiency and Purchased Feed N Input of the livestock system component; N Eco-Efficiency of the milk production component. Farm evaluation, based on the complete list of selected indicators demonstrated organic farming resulted in uniformly high values, while farms with low milk-producing herds resulted in uniformly low values. Yet on other farms, the environmental quality varied greatly when different groups of pressure indicators were considered, which highlighted the importance of expanding environmental analysis to effects within the farm. Statistical analysis demonstrated positive correlations between all farmer decision and pressure group indicators. Consumption of mineral fertiliser and pesticide negatively influenced the cropping system. Furthermore, stocking rate was found to correlate positively with the milk production component and negatively with the farm system. This study provides baseline references for ex ante policy evaluation, and monitoring tools for analysis both in itinere and ex post environment policy implementation.

Prediction of the dry-milling performance of maize hybrids through hardness-associated properties.

BACKGROUND: The hardness of kernels determines the dry-milling processing performance of maize hybrids. The identification of the best maize hybrids for the dry-milling process requires a limited number of simple, practical and reliable tests that are able to predict the potential grit yield. RESULTS: A total of 119 samples from different genetic and environmental backgrounds, collected over 3 years, were analysed for coarse/fine ratio (C/F), floating test (FLT), protein content (PC), kernel sphericity (S), total milling energy (TME) and test weight (TW). The total grit yield (TGY) of each sample was obtained through a micromilling procedure based on the manual separation of kernel endosperm followed by grinding and sieving under standard operational conditions. The TGY was used to establish the capability of the tests to predict the dry-milling aptitude. Single and multiple linear regression analyses were performed to establish equations for the prediction of TGY using C/F, FLT, PC, S, TME and TW as independent variables. The analyses were performed on three data sets, clustered year by year of the sample collection and analysis, and the resulting average coefficients of determination (R(2)) were compared by analysis of variance. C/F, FLT, TME and, to a lesser extent, TW appeared to be easy-to-use independent descriptors of maize dry-milling. An improved model prediction ability was observed when different combinations of a few physical and chemical properties were used as input variables. However, the models in which three or more variables were used did not lead to any significant improvement in TGY prediction compared with the smaller models. CONCLUSION: This study contributes towards establishing the best predictor of maize kernel aptitude to dry-milling processes. Of all considered data sets, a milling evaluation factor (C/F or TME) coupled with kernel density (measured by means of the FLT) showed the best predictive ability for dry-milled product yields.