Phenotyping for waterlogging tolerance as a proxy for Phytophthora medicaginis resistance in chickpea

Phytophthora root rot (PRR) caused by the soil-borne oomycete Phytophthora medicaginis is a significant constraint to chickpea (Cicer aretinium) production across the northern grains region of Australia. In flooded soil, which is conducive to PRR disease development, up to 70% yield loss can occur in the most resistant Australian cultivars. Incorporating waterlogging tolerance in soybean (Glycine max) has been shown to improve quantitative resistance to Phytophthora sojae. Root growth of three chickpea genotypes were assessed at the seedling stage under waterlogging, PRR and the combination of these abiotic and biotic constraints. Levels of waterlogging tolerance in chickpea are inherently low; yet selected genotypes displayed variability in root traits linked to improved waterlogging tolerance. The PRR moderately susceptible chickpea cultivar Yorker and PRR very susceptible Rupali demonstrated an eight-fold increase in early adventitious root growth from the epicotyl region under waterlogging stress, compared to the PRR resistant interspecific backcross genotype 04067-81-2-1-1 (C. echinospermum x C. aretinium*2). Selection for primary root depth, which was significantly greater in 04067-81-2-1-1 under waterlogging, appears to improve PRR resistance compared with root replacement traits. Soil-borne Phytophthora spp. are reportedly attracted to branch sites and leached exudates. We propose that compromised root barriers at emergence sites of adventitious roots under waterlogging conditions hasten hyphal entry, potentially increasing susceptibility to PRR. Hence, screening for root depth and absence of adventitious root development under waterlogged conditions may offer a novel proxy phenotyping method for PRR resistance traits at early stages of chickpea breeding.

Coupled modelling of hydrological processes and grassland production in two contrasting climates

Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (Generalized Likelihood Uncertainty Estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model accurately reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry matter allocation below-ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

A Comparative Analysis of Root Growth Modules in HYDRUS for SWC of Rice under Deficit Drip Irrigation

Root distribution during rice cultivation is a governing factor that considerably affects soil water content (SWC) and root water uptake (RWU). In this study, the effects of activating root growth (using growth function) and assigning a constant average root depth (no growth during simulation) on SWC and RWU for rice cultivation under four deficit drip irrigation treatments (T90, T80, T70, and T60) were compared in the HYDRUS-2D/3D model version 3.03. A secondary objective was to investigate the effect of applied deficit irrigation treatments on grain yield, irrigation water use efficiency (IWUE), and growth traits of rice. The simulated DI system was designed to reflect a representative field experiment implemented in El-Fayoum Governorate, Egypt, during two successive seasons during 2017 and 2018. The deficit treatments (T90, T80, T70, and T60) used in the current study represent scenarios at which the first irrigation event was applied when the pre-irrigation average SWC within the upper 60 cm of soil depth was equal to 90%, 80%, 70%, and 60% of plant-available water, respectively. Simulation results showed that as water deficiency increased, SWC in the simulation domain decreased, and thereby, RWU decreased. The average SWC within the root zone during rice-growing season under different deficit treatments was slightly higher when activating root growth function than when considering constant average root depth. Cumulative RWU fluxes for the case of no growth were slightly higher than for the case of root growth function for T90, T80, and T70 accounting for 1289.50, 1179.30, and 1073.10 cm2, respectively. Average SWC during the growth season (24 h after the first irrigation event, mid-season, and 24 h after the last irrigation event) between the two cases of root growth was strongly correlated for T90, T80, T70, and T60, where r2 equaled 0.918, 0.902, 0.892, and 0.876, respectively. ANOVA test showed that there was no significant difference for SWC between treatments for the case of assigning root growth function while the difference in SWC among treatments was significant for the case of the constant average root depth, where p-values equaled 0.0893 and 0.0433, respectively. Experimental results showed that as water deficiency decreased, IWUE increased. IWUE equaled 1.65, 1.58, 1.31, and 1.21 kg m−3 for T90, T80, T70, and T60, respectively. Moreover, higher grain yield and growth traits of rice (plant height, tillers number plant−1, panicles length, panicle weight, and grain number panicles−1) were obtained corresponding to T90 as compared with other treatments. Activating the root growth module in HYDRUS simulations can lead to more precise simulation results for specific dates within different growth stages. Therefore, the root growth module is a powerful tool for accurately investigating the change in SWC during simulation. Users of older versions of HYDRUS-2D/3D (version 2.05 and earlier) should consider the limitations of these versions for irrigation scheduling.

Mathematical modelling of surface irrigation for field crops in Jordan based on soil hydrological-physical properties

Jordan suffers from drought and depletion of water resources. In-field crop management, the issue of irrigation scheduling is important and influential. In this research note, a simple method was developed for scheduling surface irrigation of field crops based on inputs of crop ecology, effective root depth, soil texture, soil hydrology, and logical mathematics. It was concluded that the science of mathematics has succeeded to meet academic irrigation scheduling in terms of surface irrigation for field crops based on both soil hydrological and physical traits. Extension scholar has a decision to choose mathematical irrigation model depends on the traditional inputs or updating the model by searching for renewable inputs such as different varieties root depths, optimum row spacing of each crop, drip irrigation mathematical modelling, and digital sensing. In both cases, the input related to the effective root depth is a major and basic factor in mathematical irrigation scheduling. It is, therefore, recommendable that extension research-based systems should focus on basic mathematics to capacitate the complementary role of academics, research, and extension in irrigation modelling, and rural development.

The reduction of systematic temperature biases in soil moisture-limited regimes by stochastic root depth variations

In soil moisture-limited evapotranspiration regimes, near-surface temperatures are strongly affected by the available soil water amount for evapotranspiration. Its spurious representation in climate models consequently results in an inaccurately simulated turbulent heat flux partitioning and associated temperature biases.Since the physical reasons for soil moisture induced temperature biases are different in every region and model, a new method is presented to reduce these biases systematically. To achieve this, a stochastic root depth variation is applied, whereby the root depths in each grid-box of the model domain are uniformly perturbed. Thus, the soil water supply for evapotranspiration is increased for 50 % of the grid-boxes in the model domain and reduced for the other 50 %. In energy-limited regimes, where soil moisture just slightly affects the near-surface temperatures, the turbulent heat flux partitioning is not affected. In moisture-limited regimes, the method has an asymmetric effect on evapotranspiration. In cases of overestimated supplies, the reduced root depths in 50 % of the model domain result in an overall evapotranspiration reduction. In cases of underestimated supplies, the opposite is the case. In cases of correctly simulated supplies, the evapotranspiration reduction in 50 % of the model domain and the evapotranspiration increase in the other 50 % balance each other on a climatological mean. In this way, the method affects the turbulent heat flux partitioning only if soil moisture is spuriously simulated in the model. The associated biases are then systematically reduced, independently of the underlying physical process, which caused the soil moisture deficiencies.

Alteration of dry matter accumulation under soil moisture fluctuation stress in rice (Oryza sativa L.)

Drought-flood abrupt alterations (DFAA) is a condition in drought season when sudden rain inundate rice plants. These events are due to the high frequency of extreme climate events that might pose a threat to rice productivity. DFAA causes cumulative stress on rice which affects crop growth and alters dry matter accumulation. This study aims to understand the effect of DFAA to dry matter accumulation by assessing six rice varieties under DFAA. Three treatments were provided such as continuously irrigated as non-water stress (NS) as a control; drought to water stress -35 kPa (DFAA1) followed by sudden flood; drought to severe water stress -70 kPa (DFAA2) followed by abrupt floods; repeated until harvest. The study found that the alteration of dry matter accumulation was determined by root length, root weight, shoot length and shoot weight. Only varieties that are able to increase root depth under water stress fluctuation will be able to maintain the yield. The results of study showed that root depth was positively correlated with shoot length (r = 0.68), shoot weight (r = 0.62), root weight (r = 0.57), percentage of filled grain (r = 0.55) and number of filled grain per hill (r = 0.49). Shoot length was positively correlated with shoot weight (r = 0.83), root weight (r = 0.75) and the number of filled grain (r = 0.62), while shoot weight was only positively correlated with root weight (r = 0.88). This means that only root depth and shoot length can increase the seed setting rate and the number of filled grains per hill. Furthermore, at DFAA2, the percentage of filled grain was highest in Sipulo followed by Bo Santeut, Sanbei, Towuti and Situ Patenggang, which mean that varieties with deeper and heavier root dry weight can maintain higher yields than shallow and low root dry weight. The result of the study may allow to select rice varieties that are resistant to multilevel water-stress and able to maintain the potential yield, by looking at root depth, root dry weight, and through their grain yield in general. These traits could become key indicators for resistance to DFAA stress in rice. It is also necessary to pay attention to the fluctuation of soil water content in critical phases, especially in the reproductive phase and grain filling

Investigation of Heavy Metalloid Pollutants in the Earth's Environment Using Kriging Method and Hydrus Model

The zoning of copper, nickel and lead heavy metals was investigated by using Kriging method in GIS environment using circular, spherical, exponential and Gaussian variorums. In addition, one-dimensional Hydrus modeling of water flow and heavy metals in the soil environment was simulated up to 50 cm depth for a 210-day period and the concentration of heavy metals to the root .depth was simulated. Distribution of lead element in soil surface with spherical model showed that its variation was in the range of 20 to 70 mg / kg. These values were 50-60 mg / kg for copper and 30 mg / kg for nickel. Investigation of heavy metals in the soil using the Hydrus model showed that the simulated value at the initial 0-15 cm depth has the highest value and at lower depths is decreased. Comparison of the concentrations of these elements with the standard allowed by the WHO showed that the lead element in this region was higher than the permissible level.