Soil salinization in arid and coastal areas poses a significant threat to crop production, which is further aggravated by climate change and the over-exploitation of aquifers. Cultivation of salt and drought-tolerant crops such as quinoa represents a promising adaptation pathway for agriculture in saline soils. Quinoa (Chenopodium quinoa Willd.) is a salt-loving plant, known for its tolerance to drought and salinity using complex stress responses. However, available models of quinoa growth are limited, particularly under salinity stress. The objective of this study was to calibrate the crop growth, and salinity and drought stress parameters of the SWAP - WOFOST model and evaluate whether this model can represent quinoa's stress tolerance mechanisms. Field experimental data were used from two quinoa varieties: ICBA-Q5 grown under saline conditions in Laayoune, Morocco, in 2021, and Bastille grown under rainfed, non-saline conditions in Merelbeke, Belgium, from 2018 to 2023. Calibration and parameter uncertainty was performed using the DiffeRential Evolution Adaptive Metropolis (DREAMzs) algorithm on key parameters identified via sensitivity analysis using the Morris method. The resulting crop parameters provide insights into the stress tolerance mechanisms of quinoa, including reduction of transpiration and uptake of solutes. The salinity stress function of SWAP effectively represents these tolerance mechanisms and accurately predicts the impact on yield, under arid conditions. Under Northwestern European climate, the model replicates the impact of drought stress on yield. The calibrated model offers perspectives for evaluating practices to reduce soil salinization in arid conditions and for modeling crop performance under water-limited conditions or future salinization in temperate regions.

Lentils in Australia are primarily grown in temperate and Mediterranean climates, especially in the southern and western regions of the country. As in other parts of the world, lentil yields in these areas are significantly influenced by factors such as frost, heat, and drought, contributing to variable production. Therefore, selecting appropriate lentil varieties and determining optimal sowing times that align with favourable growing conditions is crucial. Accurate predictions of crop development are essential in this context. Current models mainly rely on photoperiod and temperature to predict lentil phenology; however, they often neglect the impact of soil water on flowering and pod set. This study investigated whether incorporating soil water as an additional factor could improve predictions for these critical growth stages. The modified model was tested using 281 data points from various lentil experiments that examined the timing of flowering (61-147 days) and pod set (77-163 days) across different combinations of location, variety, sowing time, and season. The results indicated that including soil water in the prediction model achieved an R2 value of 0.84 for flowering and 0.83 for pod set. The normalised root mean square error (NRMSE) was 0.07, and Lin's concordance correlation coefficient (LinCCC) was 0.91. The model produced an R2 of 0.88, an NRMSE of 0.05, and a LinCCC of 0.93 flowering compared to the default model, which yielded an R2 of 0.24, an NRMSE of 0.17, and a LinCCC of 0.36 for flowering. A limited sensitivity analysis of the modified model showed that variations in initial soil water and in-season rainfall significantly affected the timing of flowering and pod set. Additionally, we employed a probability framework to assess the crop's vulnerability to the last frost day and early heat stress events during the reproductive stage. This approach provided valuable insights for decision-making to mitigate risks associated with frost and heat stress. Our study suggests that integrating soil water dynamics into lentil phenology models improves the accuracy and precision of predictions regarding the timing of flowering and pod set. These improvements lead to better forecasts, ultimately helping to minimise damage from frost and heat stress during lentil cultivation and can better explain the effect of climate variability.

The presence of discontinuities (e.g. faults, fractures, veins, layering) in crystalline rocks can be challenging for seismic interpretations because the wide range of their size, orientation, and intensity, which controls the mechanical properties of the rock and elastic wave propagation, resulting in equally varying seismic responses at different scales. The geometrical characterisation of adjacent outcrop discontinuity networks allows a better understanding of the nature of the subsurface rocks and aids seismic interpretation. In this study, we characterise the discontinuity network of the Balmuccia peridotite (BP) in the Ivrea-Verbano Zone (IVZ), northwestern Italy. This geological body is the focus of the Drilling the Ivrea -Verbano zonE (DIVE), an international continental scientific drilling project, and two active seismic surveys, SEismic imaging of the Ivrea ZonE (SEIZE) and high-resolution SEIZE (Hi-SEIZE), which aim to resolve the subsurface structure of the DIVE drilling target through high-resolution seismic imaging. For fracture characterisation, we developed two drone-based digital outcrop models (DOMs) at two different resolutions (10-3-10 m and 10-1-103 m), which allowed us to quantitatively characterise the orientation, size, and intensity of the main rock discontinuities. These properties affect the seismic velocity and consequently the interpretation of the seismic data. We found that (i) the outcropping BP discontinuity network is represented by three more sets of fractures with respect to those reported in the literature; (ii) the discontinuity sizes follow a power-law distribution, indicating similarity across scales, and (iii) discontinuity intensity is not uniformly distributed along the outcrop. Our results help to explain the seismic behaviour of the BP detected by the SEIZE survey, suggesting that the low P-wave velocities observed can be related to the discontinuity network, and provide the basic topological parameters (orientation, density, distribution, and aperture) of the fracture network unique to the BP. These, in turn, can be used for interpretation of the Hi-SEIZE seismic survey and forward modelling of the seismic response. (c) 2024 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

Bread wheat and durum wheat genotypes were grown in field experiments at two locations in New South Wales, Australia across several years and using two sowing times ('early' v. 'late'). Genotypes were grouped based on genetic similarity. Grain yield, grain size, soil characteristics and daily weather data were collected. The weather data were used to calculate water and heat stress indices for four key growth periods around flowering. Least absolute shrinkage and selection operator (LASSO) was used to predict grain yield and to identify the most influential features (a combination of index and growth period). A novel approach involving the crop water supply-demand ratio effectively summarized water relations during growth. LASSO predicted grain yield quite well (adjusted R-2 from 0.57 to 0.98), especially in a set of durum genotypes. However, the addition of other important variables such as lodging score, disease incidence, weed incidence and insect damage could have improved modelling results. Growth period 2 (30 days pre-flowering up to flowering) was the most sensitive for yield loss from heat stress and water stress for most features. Although one group of bread wheat genotypes was more sensitive to water stress (drought) in period 3 (20 days pre-flowering to 10 days post-flowering). Evapotranspiration was a significant positive feature but only in the vegetative phase (pre-flowering, period 1). This study confirms the usefulness of LASSO modelling as a technique to make predictions that could be used to identify genotypes that are suitable candidates for further investigation by breeders for their stress-tolerance ability.

The intensification of the hydrological cycle has increased heavy rainfall and drought events in a changing climate. However, compared to drought, the impacts of heavy rainfall on crop production are under-studied. Using field experimental data and a calibrated crop model CYGMA, we showed that excessive soil water asso-ciated with heavy rainfall events is having a detrimental effect on cowpea yields, even in the dry environments of West Africa where cowpea is an important, protein-rich cash crop. Cowpea yields are susceptible to heavy rainfall in areas with poorly drained soils, and to drought in soils that have a low water-retention capacity. The crop model captured of the main characteristics of the observed development, growth, and yield, as well as the characteristics of root-zone soil water contents and how they vary by soil type. The analysis of d4PDF factual and counterfactual climate model simulations revealed that heavy rainfall events associated with anthropogenic climate change have increased in recent decades, and that they are projected to increase in future. Further, changes in seasonal rainfall and the number of dry days would be largely absent from CMIP6 climate projections by mid-century. Reductions in cowpea yields due to excessive soil water is projected to become more frequent, and the potential damage in a 1-in-100 extremely wet year would be comparable to the damage currently experienced in droughts, irrespective of soil types. Simulations of the projected damage due to drought show that the situation will be similar to current levels, with drought remaining a major climate hazard. However, excessive soil water is projected to be a serious threat to food security in the region. Our findings indicate that, even in dry environments, cropping systems need to be implemented in order to reduce the susceptibility of soils to both drought and excessive soil water.