Sustainable apricot orchard management to improve soil fertility and water use efficiency

This 4-year on-farm study reports the effects of different agricultural practices on yield and carbon input in an apricot orchard grown in Mediterranean area. Groups of plants under local orchard management (LOM) practices (i.e., soil tillage, removing of pruning residues, mineral fertilisers) were compared with plots under sustainable orchard management (SOM) actions (i.e., cover crop, no-tillage, compost application, mulching of pruning residues). In the SOM blocks, fertilization was based on plant demand and soil availability and irrigation volumes were calculated on the evapotranspiration values basis, while in the LOM plots fertilization and irrigation were empirically managed. Results show that yield was enhanced by 28% by SOM. In comparison with LOM plots, changed practices increased the amount of N, P, K annually incorporated into soil thus increasing their reservoir in the soil. The study demonstrates that appropriate crop management can increase the mean annual carbon soil inputs from about 1.5 t ha-1 to 9.0 t ha-1 per year

Soil Fungi-Plant Interaction

Computer modelling & simulatio

Statistical models of complex brain networks: a maximum entropy approach

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying network structure is crucial to understand the brain functioning under both healthy and pathological conditions. Yet, analyzing brain networks is challenging, in part because their structure represents only one possible realization of a generative stochastic process which is in general unknown. Having a formal way to cope with such intrinsic variability is therefore central for the characterization of brain network properties. Addressing this issue entails the development of appropriate tools mostly adapted from network science and statistics. Here, we focus on a particular class of maximum entropy models for networks, i.e. exponential random graph models (ERGMs), as a parsimonious approach to identify the local connection mechanisms behind observed global network structure. Efforts are reviewed on the quest for basic organizational properties of human brain networks, as well as on the identification of predictive biomarkers of neurological diseases such as stroke. We conclude with a discussion on how emerging results and tools from statistical graph modeling, associated with forthcoming improvements in experimental data acquisition, could lead to a finer probabilistic description of complex systems in network neuroscience.Comment: 34 pages, 8 figure

Integration of the regulated deficit irrigation strategy in a sustainable orchard management system

Irrigation in arid regions requires special attention to optimize the management of all components of the orchard system in order to increase water use efficiency and reduce environmental impacts (e.g. soil salinization, degradation of ground and surface waters). This six-year study reports the comparison of some orchard practices (soil and irrigation management, plant nutrition) routinely adopted by local farmers (conventional, C) with those interventions having the potential to save water and maximize water use efficiency in a peach orchard and therefore defined as sustainable (S). Due to the relative approach (C versus S) used in this study, classical statistical comparison of results could not be made. The S system included the application of regulated deficit irrigation (RDI) with specific crop coefficients to calculate the plant water requirement. The S system on average saved 1450 m3 ha-1 of water per year without affecting yield or fruit quality. The concept of economic water productivity (EWP) is discussed. We conclude that addressing some practices currently adopted by farmers could increase sustainability of irrigation and enhance (EWP) in peach tree orchards

Statistical Genetics and Direct Coupling Analysis beyond Quasi-Linkage Equilibrium

This work is about statistical genetics, an interdisciplinary topic between Statistical Physics and Population Biology. Our focus is on the phase of Quasi-Linkage Equilibrium (QLE) which has many similarities to equilibrium statistical mechanics, and how the stability of that phase is lost. The QLE phenomenon was discovered by Motoo Kimura and was extended and generalized to the global genome scale by Neher & Shraiman (2011). What we will refer to as the Kimura-Neher-Shraiman (KNS) theory describes a population evolving due to the mutations, recombination, genetic drift, natural selection (pairwise epistatic fitness). The main conclusion of KNS is that QLE phase exists at sufficiently high recombination rate ($r$) with respect to the variability in selection strength (fitness). Combining these results with the techniques of the Direct Coupling Analysis (DCA) we show that in QLE epistatic fitness can be inferred from the knowledge of the (dynamical) distribution of genotypes in a population. Extending upon our earlier work Zeng & Aurell (2020) here we present an extension to high mutation and recombination rate. We further consider evolution of a population at higher selection strength with respect to recombination and mutation parameters ($r$ and $\mu$). We identify a new bi-stable phase which we call the Non-Random Coexistence (NRC) phase where genomic mutations persist in the population without either fixating or disappearing. We also identify an intermediate region in the parameter space where a finite population jumps stochastically between QLE-like state and NRC-like behaviour. The existence of NRC-phase demonstrates that even if statistical genetics at high recombination closely mirrors equilibrium statistical physics, a more apt analogy is non-equilibrium statistical physics with broken detailed balance, where self-sustained dynamical phenomena are ubiquitous

Soil water availability and relationship between canopy and roots in young olive trees (cv Coratina).

Trials were carried out in the Basilicata region (41°03’ N, 15°42’ E, Southern Italy) using ownrooted plants of the cultivar Coratina planted in 1992 at distances of 6 x 3 m. During 1992, the whole plot (about 7000 m2) was irrigated. From 1993 onwards, irrigation was suspended in part of the plot. A representative number of plants during 1992, 1993, 1994 and 1998 was destroyed in order to carry out dry weight measurements on roots and canopy. The ratio between root and leaf dry weight was always greater in nonirrigated plants compared to irrigated ones. Roots explored a soil volume ranged from 0.5 m3 in the first year to 16.8 m3 in the seventh year for irrigated plants and from 0.5 m3 to 13.4 m3 for non-irrigated ones. The study showed that in deep soil, with a greater capacity for water storage during the rainfall season, limited water supply (220-1350 m3 ha-1) during the first seven years from planting increased canopy growth by 79% compared to nonirrigated plants, but made little difference to root growth. In non-irrigated plants, canopy growth (but not root growth) was drastically reduced, as a defence strategy against water deficit, making for a better root/leaf ratio and consequently greater water availability for leaves