Crop rotational diversity can mitigate climate-induced grain yield losses.

Diversified crop rotations have been suggested to reduce grain yield losses from the adverse climatic conditions increasingly common under climate change. Nevertheless, the potential for climate change adaptation of different crop rotational diversity (CRD) remains undetermined. We quantified how climatic conditions affect small grain and maize yields under different CRDs in 32 long-term (10-63 years) field experiments across Europe and North America. Species-diverse and functionally rich rotations more than compensated yield losses from anomalous warm conditions, long and warm dry spells, as well as from anomalous wet (for small grains) or dry (for maize) conditions. Adding a single functional group or crop species to monocultures counteracted yield losses from substantial changes in climatic conditions. The benefits of a further increase in CRD are comparable with those of improved climatic conditions. For instance, the maize yield benefits of adding three crop species to monocultures under detrimental climatic conditions exceeded the average yield of monocultures by up to 553 kg/ha under non-detrimental climatic conditions. Increased crop functional richness improved yields under high temperature, irrespective of precipitation. Conversely, yield benefits peaked at between two and four crop species in the rotation, depending on climatic conditions and crop, and declined at higher species diversity. Thus, crop species diversity could be adjusted to maximize yield benefits. Diversifying rotations with functionally distinct crops is an adaptation of cropping systems to global warming and changes in precipitation.

LAI estimation through remotely sensed NDVI following hail defoliation in maize (Zea mays L.) using Sentinel-2 and UAV imagery.

Extreme events such as hailstorms are a cause for concern in agriculture, leading to both economic and food supply losses. Traditional damage estimation techniques have recently been called into question since damages have rarely been quantified at the large-field scale. Damage-estimation methods used by field inspectors are complex and sometimes subjective and hardly account for damage spatial variability. In this work, a normalized difference vegetation index (NDVI)-based parametric method was applied using both unmanned aerial vehicles (UAV) and Sentinel-2 sensors to estimate the leaf area index (LAI) of maize (Zea mays L.) resulting from simulated hail damage. These methods were then compared to the LAI values generated from the Sentinel-2 Biophysical Processor. A two-year experiment (2020-2021) was conducted during the maize cropping season, with hail events simulated during a range of maize developmental stages (the 8th-leaf, flowering, milky and dough stages) using a 0-40% defoliation gradient of damage intensities performed with the aid of specifically designed prototype machines. The results showed that both sensors were able to accurately estimate LAI in a nonstandard damaged canopy while requiring only the calibration of the extinction coefficient k(ϑ) in the case of parametric estimations. In this case, the calibration was performed using 2020 data, providing k(ϑ) values of 0.59 for Sentinel-2 and 0.37 for the UAV sensor. The validation was performed on 2021 data, and showed that the UAV sensor had the best accuracy (R2 of 0.86, root-mean-square error (RMSE) of 0.71). The k(ϑ) value proved to be sensor-specific, accounting for the NDVI retrieval differences likely caused by the different spatial operational scales of the two sensors. NDVI proved effective in parametrically estimating maize LAI under damaged canopy conditions at different defoliation degrees. The parametric method matched the Sentinel-2 biophysical process-generated LAI well, leading to less underestimations and more accurate LAI retrieval. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-023-09993-9.

Protein Maps for Durum Wheat Precision Harvest and Pasta Production.

The quality traits of dough and dry pasta obtained from semolina (Triticum durum Desf. var. Biensur), harvested from a 13.6 ha field, subjected to variable-rate nitrogen (N) fertilization, were assessed to test site-specific pasta production for a short supply chain. Based on the grain quality spatial distribution, two distinct areas with protein content lower or higher than 13% were delineated and harvested selectively. The rheological properties of semolina samples obtained from those areas were evaluated. Furthermore, dry pasta was produced and characterized for its cooking behaviour and sensory characteristics. Semolina was demonstrated to have rheological characteristics (i.e., gluten aggregation time and energy, as evaluated by GlutoPeak test) positively related to the protein content as well as the related pasta, showing better cooking quality. These results are driven by the high amounts of gluten proteins, as well as by the glutenin/gliadin ratio, which are indicators of the technological quality of semolina. Overall, the results indicate that segregation of the grain with >13% of protein at harvest led to the production of semolina with a higher gluten protein content and a higher glutenin/gliadin ratio and, hence, to the production of pasta with better cooking quality. Therefore, site-specific pasta could be a potential asset for a short supply chain, aiming at improving traceability, as well as environmental and economic sustainability.

Trade-offs among ecosystem services advance the case for improved spatial targeting of agri-environmental measures.

Agri-environmental measures (AEMs) are meant to foster environmentally-friendly farming techniques. The use of AEMs to enhance agroecosystem quality is still under debate due to site-specific spatial mismatches that often occur between adopted AEMs and delivered ecosystem services. Here, a site-specific approach was employed to assess the advantages and disadvantages of AEMs adopted from the Rural Development Programme and applied in the Veneto Region (NE Italy) during 2014-2020. Specifically, a DayCent model-GIS platform compared business-as-usual (BAU) and AEM scenarios. The effect of AEMs on ecosystem services was assessed by integrating high-resolution spatial data from multiple pedo-climates and land managements and combined agronomic and environmental outcomes. Results showed that AEM adoption generally improved ecosystem service delivery, especially by reducing water pollution and increasing soil fertility. Among simulated practices, permanent soil cover and minimum soil disturbance (i.e., conservation agriculture, pasture and meadow maintenance) produced the best results across the Veneto Region, despite compromises in agronomic performance due to AEM-specific commitments (e.g., narrow crop rotation in conservation agriculture, fertilizer use restrictions in pastures and meadows). Other AEMs (e.g., organic farming) appeared highly dependent on their spatial distribution and were influenced by a strong interaction between pedo-climatic characteristics (e.g., soil properties) and management techniques (e.g., type and quantity of nutrients input). The spatial-target approach is highly recommended to identify AEMs that achieve environmental quality objectives and develop indications as to where they should be encouraged to maximize ecosystem services delivery.

Is Site-Specific Pasta a Prospective Asset for a Short Supply Chain?

In the 2011-2012 season, variable-rate nitrogen (N) fertilization was applied two times during durum wheat vegetative growth in three field areas which differed in soil fertility in northern Italy. The quality traits of the mono-varietal pasta obtained from each management zone were assessed in view of site-specific pasta production for a short supply chain. To this purpose, semolina from cv. Biensur obtained from management zones with different fertility treated with N at variable rate was tested in comparison with a commercial reference (cv. Aureo) to produce short-cut pasta. Biensur semolina demonstrated to have technological characteristics positively correlated with the low-fertility zones treated with high N doses (200 and 200+15 kg/ha) and, to a lesser extent, with the high-soil-fertility zones (130 and 130 + 15 kg/ha of N). The lower quality parameters were obtained for pasta produced with wheat from medium-fertility zones, independently of the N dose applied. The derived pasta obtained from the low-fertility zones treated with high N doses had cooking and sensory properties comparable to those of pasta obtained using the reference cv. Aureo. These results are explained by the higher amounts of gluten proteins and by a higher glutenin/gliadin ratio in semolina, which are indicators of technological quality. Overall, the results indicate that segregation of the grain at harvest led to the production of semolina with higher protein content and, hence, to a higher pasta quality. Therefore, site-specific pasta could be a potential asset for a short supply chain, aiming to improve traceability and environmental and economic sustainability.

Automated image analysis and hyperspectral imagery with enhanced dark field microscopy applied to biochars produced at different temperatures.

Biochar from agricultural biomasses and solid wastes represents a win-win solution for a rational waste management. Its sustainable usage requires the identification and standardization of biochar characteristics. The aim of this work was to identify the physical-chemical and spatial characteristics of biochars from pruning residues (PR), poultry litter (PL), and anaerobic cattle digestate (CD) at two pyrolysis temperatures (350 °C and 550 °C). The biochar characterization was carried out by applying emerging imaging techniques, 2D automated optical image analysis and hyperspectral enhanced dark-field microscopy (EDFM), and by SEM analysis. As predictable, the feedstock composition and the pyrolysis temperature strongly influence the physical structures of the biochar samples. Irrespective of charring temperature, PR biochar was mainly characterized by a broken and fragmented structure with an irregular and rough particle surface, completely different from the original PR wood cell. The EDFM imaging analysis evidenced the thermal degradation of PR vegetal products, composed primarily of hemicellulose, cellulose and lignin. On the contrary, small and regular particles with a smooth surface were produced by the PL pyrolysis, especially at 550 °C, due to the lower PL morphological homogeneity in comparison with the other biomasses. Finally, CD charring at both temperatures was characterized by changes in chemical composition, suggested by a lower pixel intensity. In conclusion, the emerging imaging techniques used in this study proved to be very effective in analyzing some properties of biochars, and can, therefore be considered as promising experimental strategies for detecting the feedstock and pyrolysis temperature of biochar.

Degradation of soil fertility can cancel pollination benefits in sunflower.

Pollination and soil fertility are important ecosystem services to agriculture but their relative roles and potential interactions are poorly understood. We explored the combined effects of pollination and soil fertility in sunflower using soils from a trial characterized by different long-term input management in order to recreate plausible levels of soil fertility. Pollinator exclusion was used as a proxy for a highly eroded pollination service. Pollination benefits to yield depended on soil fertility, i.e., insect pollination enhanced seed set and yield only under higher soil fertility indicating that limited nutrient availability may constrain pollination benefits. Our study provides evidence for interactions between above- and belowground ecosystem services, highlighting the crucial role of soil fertility in supporting agricultural production not only directly, but also indirectly through pollination. Management strategies aimed at enhancing pollination services might fail in increasing yield in landscapes characterized by high soil service degradation. Comprehensive knowledge about service interactions is therefore essential for the correct management of ecosystem services in agricultural landscapes.

Time course of biochemical, physiological, and molecular responses to field-mimicked conditions of drought, salinity, and recovery in two maize lines.

Drought and salinity stresses will have a high impact on future crop productivity, due to climate change and the increased competition for land, water, and energy. The response to drought (WS), salinity (SS), and the combined stresses (WS+SS) was monitored in two maize lines: the inbred B73 and an F1 commercial stress-tolerant hybrid. A protocol mimicking field progressive stress conditions was developed and its effect on plant growth analyzed at different time points. The results indicated that the stresses limited growth in the hybrid and arrested it in the inbred line. In SS, the two genotypes had different ion accumulation and translocation capacity, particularly for Na(+) and Cl(-). Moreover, the hybrid perceived the stress, reduced all the analyzed physiological parameters, and kept them reduced until the recovery. B73 decreased all physiological parameters more gradually, being affected mainly by SS. Both lines recovered better from WS than the other stresses. Molecular analysis revealed a diverse modulation of some stress markers in the two genotypes, reflecting their different response to stresses. Combining biochemical and physiological data with expression analyses yielded insight into the mechanisms regulating the different stress tolerance of the two lines.

Simultaneous monitoring of soil water content and salinity with a low-cost capacitance-resistance probe.

Capacitance and resistivity sensors can be used to continuously monitor soil volumetric water content (θ) and pore-water electrical conductivity (EC(p)) with non-destructive methods. However, dielectric readings of capacitance sensors operating at low frequencies are normally biased by high soil electrical conductivity. A procedure to calibrate capacitance-resistance probes in saline conditions was implemented in contrasting soils. A low-cost capacitance-resistance probe (ECH2O-5TE, 70 MHz, Decagon Devices, Pullman, WA, USA) was used in five soils at four water contents (i.e., from dry conditions to saturation) and four salinity levels of the wetting solution (0, 5, 10, and 15 dS · m-1). θ was accurately predicted as a function of the dielectric constant, apparent electrical conductivity (EC(a)), texture and organic carbon content, even in high salinity conditions. Four models to estimate pore-water electrical conductivity were tested and a set of empirical predicting functions were identified to estimate the model parameters based on easily available soil properties (e.g., texture, soil organic matter). The four models were reformulated to estimate EC(p) as a function of EC(a), dielectric readings, and soil characteristics, improving their performances with respect to the original model formulation. Low-cost capacitance-resistance probes, if properly calibrated, can be effectively used to monitor water and solute dynamics in saline soils.