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We have studied the nucleotide diversity of common bean, Phaseolus vulgaris, which is characterized by two independent domestications in two geographically distinct areas: Mesoamerica and the Andes. This provides an important model, as domestication can be studied as a replicate experiment. We used nucleotide data from five gene fragments characterized by large introns to analyse 214 accessions (102 wild and 112 domesticated). The wild accessions represent a cross-section of the entire geographical distribution of P. vulgaris. A reduction in genetic diversity in both of these gene pools was found, which was three-fold greater in Mesoamerica compared with the Andes. This appears to be a result of a bottleneck that occurred before domestication in the Andes, which strongly impoverished this wild germplasm, leading to the minor effect of the subsequent domestication bottleneck (i.e. sequential bottleneck). These findings show the importance of considering the evolutionary history of crop species as a major factor that influences their current level and structure of genetic diversity. Furthermore, these data highlight a single domestication event within each gene pool. Although the findings should be interpreted with caution, this evidence indicates the Oaxaca valley in Mesoamerica, and southern Bolivia and northern Argentina in South America, as the origins of common bean domestication.

The forecasting of crop yield is one of the most critical research areas in crop science, which allows for the development of decision support systems, optimization of nitrogen fertilization, and food safety. Many tested modeling approaches can be differentiated according to the models and data used. The models used are traditional crop models that require data that are often difficult to measure. New modeling approaches based on artificial intelligence algorithms have proven to be of high performance, flexible, and can be tested based on available data. In this study, four independent field experiments conducted on Triticum turgidum subsp. durum Desf. in central–southern Italy were used to train a set of machine learning (ML) algorithms to predict the yield using 16 variables: fertilization, nitrogen management, pedoclimatic, and remote sensing data. Four ML algorithms were calibrated and validated over two independent sites, and a linear regression model was used as a control. The calibrated models can predict the grain yield in the two regions by using ancillary data, topsoil physical and chemical properties, multispectral drone imagery, climatic data, and nitrogen fertilizer applied at the site. Among the four ML algorithms, stochastic gradient boosting (root-mean-square error  = 0.58 t ha−1) outperformed others during calibration and transferability. Nitrogen application rate, seasonal precipitation, and temperature are the most important features for predicting wheat yield.

Natural covering (NATC) has spread on hillside vineyards of central Italy as a replacement for tillage to reduce soil erosion, although it increased nitrogen and water needs. Therefore, in the current context of global warming, using cover crops (CCs) that require less water and provide nitrogen becomes crucial. The effects of two low-competition legume-enriched CCs in a rainfed hillside vineyard—a perennial legume–grass mixture (PLGM) and an annual legume cover crop of Trifolium alexandrinum (ALTA)—were compared with NATC over three years. PLGM and ALTA provided good levels of soil coverage, slightly lower than NATC, which had a negligible presence of legumes. PLGM and ALTA, due to low competition, enhanced vine vigor, resulting in thicker and wider canopies (as indicated by total leaf area and leaf layer number), higher pruning weight, and increased yield. PLGM and ALTA led to good qualitative levels, with higher grapes acidities, lower pH and total soluble solids content and, additionally, significantly higher yeast assimilable nitrogen content. In conclusion, implementing low-competition legume species in CCs is an effective tool to avoid soil erosion in a climate change scenario, leading to increased productivity, higher acidity, and improved nitrogen content in the grapes.

Precision agriculture (PA) is an agronomic management that allows the parsimonious use of agronomic inputs according to the crop's actual spatiotemporal needs. The most important agronomic practice of PA is the site‐specific management (SSM) of agronomic inputs. To apply SSM, the number and location of homogeneous zones must be defined. Nowadays, many approaches are used to define the number of zones in an arbitrary and constant way, such as high‐, medium‐, and low‐input zones, without a priori statistical analysis. Two combined field experiments were carried out on durum wheat (Triticum turgidum subsp. durum Desf.) in central Italy to agronomically validate an automatic approach aimed at defining the number and position of the homogeneous zones using multispectral images. For both sites, at stem elongation and anthesis, the dry biomass per square meter and Soil Plant Analysis Development (SPAD) readings were measured, while at crop maturity the yield was measured. This approach uses multispectral images as source data and uses the gap statistic index to determine the correct number of zones. The management zones created were well‐fitted with the experimental design of both sites, and the validation was made by the statistical analysis performed on yield data, SPAD, dry biomass, and normalized difference red edge index with an average difference of 40.2%, 28.5%, 57.5%, and 37.4%, respectively, between the management zones. The approach could be scaled by using the multitemporal multispectral images provided by satellite constellation, which enables farms and societies to take advantage of all the economic and environmental benefits of PA. Core Ideas Multi‐site/year field experiments were carried out on durum wheat in central Italy. Agronomic validation of automatic zone management delineation approach was set up. The management zone well‐fitted with the experimental sites. The approach could be scaled up by using satellite multispectral data.

Organic farming is claimed to improve soil fertility. Nonetheless, among organic practices, net C-inputs may largely vary in amount and composition and produce different soil conditions for microbial activity and plant-root system adaptation and development. In this study, we hypothesised that, in the regime of organic agriculture, soil chemical and biochemical properties can substantially differ under contrasting crop rotation systems and produce conditions of soil fertility to which the plant responds through diverse growth and production. The impact of 13 years of alfalfa-crop rotation (P-C) and annual crop rotation (A-C) was evaluated on the build up of soil organic carbon (SOC), active (light fraction organic matter, LFOM; water soluble organic carbon, WSOC) and humic fraction [fulvic acids carbon (FAC), humic acids carbon (HAC)], soil biochemical properties [microbial biomass carbon (MBC), basal respiration (dBR), alkaline phosphatase (AmP), arylsulfatase (ArS), orto-diphenoloxidase (o- DPO)] and the amount of available macro-nutrients (N, P, and S) at two different soil depths (0-10 cm and 10-30 cm) before and after cultivation of wheat. We also studied the response of root morphology, physiology and yield of the plant-root system of wheat. Results showed that the level of soil fertility and plant-root system behaviour substantially differed under the two crop rotation systems investigated here. We observed high efficiency of the P-C soil in the build up of soil organic carbon, as it was 2.9 times higher than that measured in the A-C soil. With the exception of o-DPO, P-C soil always showed a higher level of AmP and ArS activity and an initial lower amount of available P and S. The P-C soil showed higher rootability and promoted thinner roots and higher root density. In the P-C soil conditions, the photosynthesis and yield of durum wheat were also favoured. Finally, cultivation of wheat caused an overall depletion of the accrued fertility of soil, mainly evident in the P-C soil, which maintained a residual higher level of all the chemical and biochemical properties tested.

Reduction of soil greenhouse gas emissions is crucial to control increases in atmospheric CO2 concentrations. Permanent grasslands are of considerable importance in climate change mitigation strategies as they cover about 13% of the global agricultural area. However, uncertainties remain for the effects of management practices on soil respiration, especially over the short term. This study investigated the influence of different mowing intensities on soil respiration over the short term for Bromus erectus-dominated grasslands in the central Apennines. From 2016 to 2018, soil respiration, temperature, and moisture were measured under three different management systems: customary management, intensive use, and abandonment. Both soil water content and temperature changed over time, however mowing did not affect soil water content while occasionally altered soil temperature. The intensive use promoted higher seasonal mean soil respiration compared to the abandonment only during the 2016 growing season. Soil temperature was the main driver of soil respiration above a soil water content threshold that varied little among treatments (18.23–22.71%). Below the thresholds, soil moisture was the main driver of soil respiration. These data suggest that different mowing regimes have little influence on soil respiration over the short term in Bromus erectus-dominated grasslands. Thus, more intensive use would not have significative impacts on soil respiration, at least over the short term. Future studies need to clarify the role of root mycorrhizal and microbial respiration in the light of climate change, considering the seasonal redistribution of the rainfall.

Traditional land-use planning models have proven inadequate to address contemporary issues in sustainable development and protection governance. In recent years, new ‘performance based’ approaches that integrate ecosystem services (ES) provided via green infrastructure (GI) into traditional spatial planning models have been proven to reach a higher level of environmental performance, necessary to improve quality of life for all people. In Italy, there are no mandatory planning instruments to design and manage GI, which still remains a component of the traditional land-use plan. Here, the development of urban green spaces (UGS) based on ‘quantitative assessment’ is not suitable for guaranteeing the supply of ES. In addition, the scarcity of financial resources to develop ‘green standards’, as prescribed in the land-use plan to strategically design the GI, is an issue for most Italian public administrations. The paper provides the results of a test case conducted in a public green area of the city of Ancona, where the experimentation of a diversified maintenance strategy of an urban lawn significantly reduced the management cost and improved the environmental performance of green spaces. The identification of a unified management strategy to be applied to all the public UGS can help to achieve better results in support of sustainability, to redesign the continuity of GI and to develop strategies for future urban green master plans.

PurposeThis research aims to develop a meta-machine learning model to optimize soil and nitrogen management for durum wheat in Italy. It addresses the challenges of increased food production on limited land amidst rising input costs, geopolitical changes, and climate change. The goal is to aid decision-makers in achieving maximum crop yield and income margins through effective agronomic strategies.MethodsThe study developed a meta-machine learning model, integrating classification and regression models, and tested it at four sites in Marche and Basilicata, Italy, over several years. The model incorporated data from remote sensing, crop phenology, soil chemical properties, weather data, soil management, and nitrogen levels. A Random Forest model was used to classify crop phenology, while a Neural Network model predicted yield. Eleven nitrogen levels were compared across these sites.ResultsThe Random Forest model achieved an accuracy of 0.98, kappa of 0.96, and recall of 0.98 for predicting crop phenology. The Neural Network model for yield prediction had an R squared of 0.90 and a Root Mean Square Error of 0.59 t ha-1. Key factors identified for model accuracy were temperature, precipitation, NDVI, and nitrogen input. Simulations of 30 soil management and fertilization combinations revealed that no-tillage management increased grain yield. The Marginal Fertilizer Yield Index determined optimal nitrogen application.ConclusionsThe meta-machine learning model accurately predicted durum wheat yield and identified effective agronomic strategies, demonstrating the potential for broader application in field conditions. The model offers a promising approach to sustainable agriculture and climate change mitigation by utilising publicly available spatial datasets.

Zygotic embryo and shoot tip explants of Phoenix canariensis were cultured on MS (1962) basal medium supplemented with 100 micromolar Picloram and 9.5 micromolar kinetin or 10.8 micromolar or 45.25 micromolar 2,4-dichlorophenoxyacetic acid (2,4-D) and 9.8 micromolar N6-(2-isopentenyl) adenine (2iP). These explants after 12 weeks in darkness at 28 degrees C, produced embryogenic callus with very compact, pale yellow, nodular structures. Proliferation and maintenance of embryogenic callus was on MS basal medium with 2.26 micromolar 2,4-D, 0.83 micromolar kinetin and 2 micromolar abscisic acid (ABA), with a regular subculture every 3-4 weeks. Somatic embryo development was promoted by two months of culture on MS liquid medium enriched with 2 micromolar ABA, for torpedo stage development, then on liquid MS medium with 1 micromolar N6-benzyladenine (BA) and 0.46 micromolar kinetin, for shoot induction. Germinated embryos were transferred to basal media enriched with 0.45 micromolar BA and 0.06 micromolar naphthaleneacetic acid (NAA) for root and shoot induction and elongation. Viable plants were recovered on basal MS free of plant growth regulators (PGRs), but percentages of plant conversion have to be improved.