Effect of soil moisture stress on growth light azimuth and yield of dryland sorghum

Based on weekly data for 4 years (1986-1989) at Bellary, soil moisture balance for rabi sorghum has been worked out for both irrigated and non-irrigated conditions. These soil moisture values have been used to identify periods of water stress which the crop experienced. during the growth cycle. The extent of yield reduction due to the stress was then evaluated from the actual soil water content and total available water extent and discussed. The ratio of evapotranspiration to potential evapotranspiration and water requirement of the crop has also been worked out to assess the stress situation of the crop during its growing period. This ratio has been found to be related to moisture availability at the root zone.

Use of Phenomics for Differentiation of Mungbean (Vigna radiata L. Wilczek) Genotypes Varying in Growth Rates Per Unit of Water

In the human diet, particularly for most of the vegetarian population, mungbean (Vigna radiata L. Wilczek) is an inexpensive and environmentally friendly source of protein. Being a short-duration crop, mungbean fits well into different cropping systems dominated by staple food crops such as rice and wheat. Hence, knowing the growth and production pattern of this important legume under various soil moisture conditions gains paramount significance. Toward that end, 24 elite mungbean genotypes were grown with and without water stress for 25 days in a controlled environment. Top view and side view (two) images of all genotypes captured by a high-resolution camera installed in the high-throughput phenomics were analyzed to extract the pertinent parameters associated with plant features. We tested eight different multivariate models employing machine learning algorithms to predict fresh biomass from different features extracted from the images of diverse genotypes in the presence and absence of soil moisture stress. Based on the mean absolute error (MAE), root mean square error (RMSE), and R squared (R2) values, which are used to assess the precision of a model, the partial least square (PLS) method among the eight models was selected for the prediction of biomass. The predicted biomass was used to compute the plant growth rates and water-use indices, which were found to be highly promising surrogate traits as they could differentiate the response of genotypes to soil moisture stress more effectively. To the best of our knowledge, this is perhaps the first report stating the use of a phenomics method as a promising tool for assessing growth rates and also the productive use of water in mungbean crop.

Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements

Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.

The development and persistence of soil moisture stress during drought across southwestern Germany

The drought of 2018 in central and northern Europe showed once more the large impact that this natural hazard can have on the environment and society. Such droughts are often seen as slowly developing phenomena. However, root zone soil moisture deficits can rapidly develop during periods lacking precipitation and meteorological conditions that favor high evapotranspiration rates. These periods of soil moisture stress can persist for as long as the meteorological drought conditions last, thereby negatively affecting vegetation and crop health. In this study, we aim to characterize past soil moisture stress events over the croplands of southwestern Germany and, furthermore, to relate the characteristics of these past events to different soil and climate properties. We first simulated daily soil moisture over the period 1989–2018 on a 1 km resolution grid, using the physically based hydrological model TRAIN. We then derived various soil moisture stress characteristics, including probability, development time, and persistence, from the simulated time series of all agricultural grid cells (n≈15 000). Logistic regression and correlation were then applied to relate the derived characteristics to the plant-available storage capacity of the root zone and to the climatological setting. Finally, sensitivity analyses were carried out to investigate how results changed when using a different parameterization of the root zone, i.e., soil based or fixed, or when assessing soil moisture drought (anomaly) instead of stress. Results reveal that the majority of agricultural grid cells across the study region reached soil moisture stress during prominent drought years. The development time of these soil moisture stress events varied substantially, from as little as 10 d to over 4 months. The persistence of soil moisture stress varied as well and was especially high for the drought of 2018. A strong control on the probability and development time of soil moisture stress was found to be the storage capacity of the root zone, whereas the persistence was not strongly linearly related to any of the considered controls. On the other hand, the sensitivity analyses revealed the increased control of climate on soil moisture stress characteristics when using a fixed instead of a soil-based root zone storage. Thus, the strength of different controls depends on the assumptions made during modeling. Nonetheless, the storage capacity of the root zone, whether it is a characteristic of the soil or a difference between a shallow or deep rooting crop, remains an important control on soil moisture stress characteristics. This is different for SM drought characteristics, which have little or contrasting relation with the storage capacity of the root zone. Overall, the results give insight to the large spatial and temporal variability in soil moisture stress characteristics and suggest the importance of considering differences in root zone soil storage for agricultural drought assessments.

What’s the impact of improved soil representations in the ECMWF land surface model and how does it affect the extremes?

<p>With increasingly higher spatial resolution and a broader applications, the importance of soil representation (e.g. soil depth, vertical discretisation, vegetation rooting) within land surface models is enhanced. Those modelling choices actually affects the way land surfaces store and regulate water, energy and also carbon fluxes. Heat and water vapour fluxes towards the atmosphere and deeper soil, exhibit variations spanning a range of time scales from minutes to months in the coupled land-atmosphere system. This is further modulated by the vertical roots' distribution, and soil moisture stress function, which control evapotranspiration under soil moisture stress conditions. Currently in the ECMWF land Surface Scheme the soil column is represented by a fixed 4 layers configuration with a total of approximately 3m depth.</p><p>In the present study we explore new configurations with increased soil depth (up to 8m) and higher vertical discretisation (up to 10 layers) including a dissociation between the treatment of water and heat fluxes. Associated with the soil vertical resolution, the vertical distribution of roots is also investigated. A new scheme that assumes a uniform root distribution with an associated maximum rooting depth is explored. The impact of these new configurations is assessed through surface offline simulations driven by the ERA5 meteorological forcing against in-situ and global products of energy, water and carbon fluxes with a particular focus on the diurnal cycle and extreme events in recent years.</p>

Evaluation of soil moisture stress parameterizations in MEGAN model against MOFLUX field data and satellite observations of formaldehyde from OMI

<p>Biogenic volatile organic compounds (BVOCs) are emitted globally at about 1,100 Tg per year, with almost half of the share entailed by isoprene. Isoprene is highly reactive in the atmosphere, and its degradation impacts the atmospheric composition through the generation of ozone (in presence of NOx typical of polluted areas) and secondary organic aerosols, which both pose a risk to human health. Extreme weather conditions like heatwaves and droughts can substantially affect the emissions of isoprene in ways that are largely unknown. This limited knowledge is owed to the scarcity of isoprene flux measurements under drought stress conditions. The Missouri Ozarks AmeriFlux (MOFLUX) site is located in a high isoprene-emitting oak-hickory forested region with recurring drought occurrences. Until today, it is the only site with isoprene flux measurements that capture drought behaviour.</p><p>In this study, we use the state-of-the-art MEGAN biogenic emission model (Guenther et al., 2006; 2012) coupled with the canopy model MOHYCAN (Müller et al., 2008) to estimate isoprene emissions and evaluate two different parameterizations of the soil moisture stress factor (γ<sub>SM</sub>): (a) the one used in MEGANv2.1, which consists of a simple dependence on soil water content and the permanent wilting point with inputs either from ERA-Interim or the GLEAMv3 reanalysis (Martens et al., 2017), and (b) the parameterization available in MEGANv3 (Jiang et al., 2018), which considers the physiological effects of drought stress on plant photosynthesis as defined in the Community Land Model (CLM4.5), which embeds the MEGAN model.  The effect of γ<sub>SM</sub> on isoprene estimates is assessed against measurements of isoprene fluxes at the MOFLUX field site collected during the mild summer drought in 2011 (Potosnak et al., 2014) and the severe drought in 2012 (Seco et al., 2015). Based on the comparisons at the MOFLUX site, we perform an optimization of the empirical parameters of the MEGANv2.1 soil moisture stress parameterization. In addition, the parameterization is further evaluated using spaceborne formaldehyde (HCHO) columns observed by the OMI sounder. To this end, we perform multiyear simulations (2005-2016) of atmospheric composition with the IMAGES global chemistry-transport model (Müller et al., 2019) using isoprene emission datasets obtained for several variants of the parameterization. We evaluate the resulting HCHO column distributions and their interannual variability against OMI HCHO columns over drought-prone regions.</p><p>This work is conducted in the frame of the ALBERI project, funded by the Belgian Science Policy Office through the STEREO III programme.</p>

GYMEE: A Global Field-Scale Crop Yield and ET Mapper in Google Earth Engine Based on Landsat, Weather, and Soil Data

In this study, we used Landsat Earth observations and gridded weather data along with global soil datasets available in Google Earth Engine (GEE) to estimate crop yield at 30 m resolution. We implemented a remote sensing and evapotranspiration-based light use efficiency model globally and integrated abiotic environmental stressors (temperature, soil moisture, and vapor deficit stressors). The operational model (Global Yield Mapper in Earth Engine (GYMEE)) was validated against actual yield data for three agricultural schemes with different climatic, soil, and management conditions located in Lebanon, Brazil, and Spain. Field-level crop yield data on wheat, potato, and corn for 2015–2020 were used for assessment. The performance of GYMEE was statistically evaluated through root-mean-square error (RMSE), mean absolute error (MAE), mean bias error (MBE), relative error (RE), and index of agreement (d). The results showed that the absolute difference between the modeled and predicted field-level yield was within ±16% for the analyzed crops in both Brazil and Lebanon study sites and within ±15% in the Spain site (except for two fields). GYMEE performed best for wheat crop in Lebanon with a low RMSE (0.6 t/ha), MAE (0.5 t/ha), MBE (−0.06 t/ha), and RE (0.83%). A very good agreement was observed for all analyzed crop yields, with an index of agreement (d) averaging at 0.8 in all studied sites. GYMEE shows potential in providing yield estimates for potato, wheat, and corn yields at a relative error of ±6%. We also quantified and spatialized the soil moisture stress constraint and its impact on reducing biomass production. A showcasing of moisture stress impact on two emphasized fields from the Lebanon site revealed that a 12% difference in soil moisture stress can decrease yield by 17%. A comparison between the 2017 and 2018 seasons for the potato culture of Lebanon showed that the 2017 season with lower abiotic stresses had higher light use efficiency, above-ground biomass, and yield by 5%, 10%, and 9%, respectively. The results show that the model is of high value for assessing global food production.

Effect of soil moisture stress at booting and flowering stages on pollen development, pollination and fertilization in upland NERICA cultivars

Spikelet sterility induced by soil moisture stress during reproductive development greatly limits grain yield in upland rice. This study aimed to elucidate differences in responses to soil moisture stress for pollen development, pollination and fertilization among upland rice cultivars. A greenhouse experiment with a split-plot design was performed for five different soil moisture treatments (T1 to T5) as the main plots and three cultivars (NERICA 1, NERICA 4 and Yumenohatamochi) as subplots, each with three replicates. Plants in pots were grown under well-watered condition (T1) and various moisture stress conditions: moderate at the booting stage (T2), severe at the booting stage (T3), moderate at the flowering stage (T4) or severe at the flowering stage (T5). During the 9-day stress period, soil moisture was maintained at -10 to -20 kPa for moderate moisture stress or -20 to -49 kPa for severe moisture stress under controlled irrigation. NERICA 1 had fewer differentiated microspores and developed pollen grains in T2 and T3 and showed poorer anther dehiscence and fewer pollen grains on the stigma than did NERICA 4 and Yumenohatamochi. NERICA 4 showed a lower percentage of basal dehiscence in T4 and T5, causing fewer pollen grains to be deposited on the stigma than for NERICA 1 and Yumenohatamochi. The results indicate that the highly sensitive process of fertilization are pollen development in NERICA 1 and pollination in NERICA 4 under soil moisture stress