scholarly journals Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1247
Author(s):  
Zhengchun Liu ◽  
Zhanjun Xu ◽  
Rutian Bi ◽  
Chao Wang ◽  
Peng He ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Data Assimilation ◽  
Winter Wheat ◽  
Soil Water ◽  
High Precision ◽  
Stress Conditions ◽  
Crop Modeling ◽  
Semiarid Regions ◽  
Arid And Semiarid

The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m−2 and 0.0305 cm3 cm−3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m−2 and 0.0244 cm3 cm−3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha−1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha−1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

scholarly journals Unexpected relationships between δ13C and wine grape performance in organic farming

OENO One ◽  
2013 ◽  
Vol 47 (4) ◽  
pp. 269 ◽  
Author(s):  
Edoardo Antonio Costantino Costantini ◽  
Alessandro Agnelli ◽  
Pierluigi Bucelli ◽  
Aldo Ciambotti ◽  
Valentina Dell’Oro ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Potential ◽  
Soil Water ◽  
Organic Farming ◽  
Water Availability ◽  
Stress Conditions ◽  
Soil Water Availability ◽  
Wine Grape ◽  
Stem Water Potential ◽  
Stem Water

<p style="text-align: justify;"><strong>Aim</strong>: To evaluate the relationship between carbon isotope ratio (δ<sup>13</sup>C) and wine grape viticultural and oenological performance in organic farming.</p><p style="text-align: justify;"><strong>Methods and results</strong>: The study was carried out for four years in the Chianti Classico wine production district (Central Italy), on five non irrigated vineyards conducted in organic farming. The reference variety was Sangiovese. Eleven sites were chosen for vine monitoring and grape sampling. The performance parameters were alcohol and must sugar content, sugar accumulation rate, mean berry weight, and extractable polyphenols. δ<sup>13</sup>C, stem water potential, and soil water availability were also monitored. Finally, soil nitrogen as well as yeast available nitrogen in the must were measured. δ<sup>13</sup>C was directly related to stem water potential and soil water deficit, and indicated a range of water stress conditions from none and moderate to strong. However, its relationship with viticultural and oenological results was contrary to expectation, that is, performance linearly increased along with soil moisture. On the other hand, the worst performance was obtained where both water and nitrogen were more limiting.</p><p style="text-align: justify;"><strong>Conclusions</strong>: The unexpected relationship between δ<sup>13</sup>C and Sangiovese performance was caused by low nitrogen availability. The studied sites all had low-fertility soils with poor or very poor nitrogen content. Therefore, in the plots where soil humidity was relatively higher, nitrogen plant uptake was favoured, and Sangiovese performance improved. Macronutrient being the main limiting factor, the performance was not lower in the plots where soil water availability was relatively larger. Therefore, the best viticultural result was obtained with no water stress conditions, at low rather than at intermediate δ<sup>13</sup>C values.</p><p style="text-align: justify;"><strong>Significance and impact of the study</strong>: Water nutrition is crucial for wine grape performance. δ<sup>13</sup>C is a method used to assess vine water status during the growing season and to estimate vine performance. A good performance is expected at moderate stress and intermediate δ<sup>13</sup>C values. A better knowledge of the interaction between water and nutrient scarcity is needed, as it can affect the use of δ<sup>13</sup>C to predict vine performance.</p>

scholarly journals A conceptual dynamic vegetation-soil model for arid and semiarid zones

2008 ◽  
Vol 12 (5) ◽  
pp. 1175-1187 ◽  
Author(s):  
D. I. Quevedo ◽  
F. Francés
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Balance ◽  
Water Availability ◽  
Leaf Biomass ◽  
Soil Conditions ◽  
Model Parameters ◽  
Arid Zones ◽  
Soil Model ◽  
Arid And Semiarid

Abstract. Plant ecosystems in arid and semiarid climates show high complexity, since they depend on water availability to carry out their vital processes. In these climates, water stress is the main factor controlling vegetation development and its dynamic evolution. The available water-soil content results from the water balance in the system, where the key issues are the soil, the vegetation and the atmosphere. However, it is the vegetation, which modulates, to a great extent, the water fluxes and the feedback mechanisms between soil and atmosphere. Thus, soil moisture content is most relevant for plant growth maintenance and final water balance assessment. A conceptual dynamic vegetation-soil model (called HORAS) for arid and semi-arid zones has been developed. This conceptual model, based on a series of connected tanks, represents in a way suitable for a Mediterranean climate, the vegetation response to soil moisture fluctuations and the actual leaf biomass influence on soil water availability and evapotranspiration. Two tanks were considered using at each of them the water balance and the appropriate dynamic equation for all considered fluxes. The first one corresponds to the interception process, whereas the second one models the evolution of moisture by the upper soil. The model parameters were based on soil and vegetation properties, but reduced their numbers. Simulations for dominant species, Quercus coccifera L., were carried out to calibrate and validate the model. Our results show that HORAS succeeded in representing the vegetation dynamics and, on the one hand, reflects how following a fire this monoculture stabilizes after 9 years. On the other hand, the model shows the adaptation of the vegetation to the variability of climatic and soil conditions, demonstrating that in the presence or shortage of water, the vegetation regulates its leaf biomass as well as its rate of transpiration in an attempt to minimize total water stress.

Simulating the Influences of Soil Water Stress on Leaf Expansion and Senescence of Winter Wheat

2020 ◽  
Vol 291 ◽  
pp. 108061 ◽  
Author(s):  
Tengcong Jiang ◽  
Zihe Dou ◽  
Jian Liu ◽  
Yujing Gao ◽  
Robert W. Malone ◽  
...  
Keyword(s):  
Water Stress ◽  
Winter Wheat ◽  
Soil Water ◽  
Leaf Expansion ◽  
Soil Water Stress

scholarly journals Subsoiling and Sowing Time Influence Soil Water Content, Nitrogen Translocation and Yield of Dryland Winter Wheat

Agronomy ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 37 ◽  
Author(s):  
Yan Liang ◽  
Shahbaz Khan ◽  
Ai-xia Ren ◽  
Wen Lin ◽  
Sumera Anwar ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Winter Wheat ◽  
Grain Yield ◽  
Soil Water ◽  
Loess Plateau ◽  
Water Storage ◽  
Soil Water Storage ◽  
Yield Reduction ◽  
Sowing Time ◽  
N Accumulation

Dryland winter wheat in the Loess Plateau is facing a yield reduction due to a shortage of soil moisture and delayed sowing time. The field experiment was conducted at Loess Plateau in Shanxi, China from 2012 to 2015, to study the effect of subsoiling and conventional tillage and different sowing dates on the soil water storage, Nitrogen (N) accumulation, and remobilization and yield of winter wheat. The results showed that subsoiling significantly improved the soil water storage (0–300 cm soil depth) and increased the contribution of N translocation to grain N and grain yield (17–36%). Delaying sowing time had reduced the soil water storage at sowing and winter accumulated growing degree days by about 180 °C. The contribution of N translocation to grain yield was maximum in glume + spike followed by in leaves and minimum by stem + sheath. Moreover, there was a positive relationship between the N accumulation and translocation and the soil moisture in the 20–300 cm range. Subsoiling during the fallow period and the medium sowing date was beneficial for improving the soil water storage and increased the N translocation to grain, thereby increasing the yield of wheat, especially in a dry year.

scholarly journals Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3161 ◽  
Author(s):  
Haizhu Pan ◽  
Zhongxin Chen ◽  
Allard de Wit ◽  
Jianqiang Ren
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Crop Growth ◽  
Open Loop ◽  
Yield Estimation ◽  
Spatiotemporal Resolution ◽  
Sentinel 2

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Simulating Gross Primary Productivity of vegetation under soil water stress using in-situ and reanalysis soil moisture data inputs

2020 ◽  
Author(s):  
Timothy Lam ◽  
Amos P. K. Tai
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Soil Water ◽  
Primary Productivity ◽  
Gross Primary Productivity ◽  
Efficiency Coefficient ◽  
Community Land Model ◽  
Soil Water Stress ◽  
Soil Moisture Data

&lt;p&gt;This study utilises in-situ and reanalysis soil moisture data inputs from various sources to evaluate the effect of soil water stress on Gross Primary Productivity (GPP) of different Plant Functional Types (PFTs) using Terrestrial Ecosystem Model in R (TEMIR), which is under development by Tai Group of Atmosphere-Biosphere Interactions (Tai et al. in prep.). An empirical soil water stress function with reference to Community Land Model (CLM) Version 4.5 is employed to quantify water stress experienced by vegetation which hinders stomatal conductance and thus carboxylation rate. The model results are compared against observations at FLUXNET sites in semi-arid regions across the globe at daily timescale where in-situ GPP data is available and water stress inhibits plant functions to some extent. By dividing the soil into two layers (topsoil and root zone), GPP simulation improves significantly comparing with using single layer bulk soil (Modified Nash-Sutcliffe Model Efficiency Coefficient N increases from -0.686 to -0.586). Such upgrade is particularly substantial for vegetation with shallow roots such as grass PFTs. Despite this improvement, the model is characterised by an overall overestimation of GPP when water stress occurs, and inconsistency of accuracy subject to PFTs and degree of water stress experienced. This study informs responses of various PFTs to soil water stress, capacity of TEMIR in simulating the responses, and possible drawbacks of empirical soil water stress functions, and highlights the importance of topsoil moisture data input for vegetation drought monitoring.&lt;/p&gt;&lt;p&gt;Keywords: Soil water stress, Terrestrial model representation, Photosynthesis, In-situ data, Reanalysis data, FLUXNET&lt;/p&gt;

scholarly journals Growth and carbon assimilation limitations in Ricinus communis (Euphorbiaceae) under soil water stress conditions

2010 ◽  
Vol 24 (3) ◽  
pp. 648-654 ◽  
Author(s):  
Tanise Luisa Sausen ◽  
Luís Mauro Gonçalves Rosa
Keyword(s):  
Water Stress ◽  
Soil Water ◽  
Drought Resistance ◽  
Ricinus Communis ◽  
Stomatal Closure ◽  
Carbon Assimilation ◽  
Stress Conditions ◽  
Carbon Gain ◽  
Resistant Species ◽  
Ricinus Communis L

Water availability may influence plant carbon gain and growth, with large impacts on plant yield. Ricinus communis (L.), a drought resistant species, is a crop with increasing economic importance in Brazil, due to its use in chemical industry and for the production of biofuels. Some of the mechanisms involved in this drought resistance were analyzed in this study by imposing progressive water stress to pot-grown plants under glasshouse conditions. Water withholding for 53 days decreased soil water gravimetric content and the leaf water potential. Plant growth was negatively and significantly reduced by increasing soil water deficits. With irrigation suspension, carbon assimilation and transpiration were reduced and remained mostly constant throughout the day. Analysis of A/Ci curves showed increased stomatal limitation, indicating that limitation imposed by stomatal closure is the main factor responsible for photosynthesis reduction. Carboxylation efficiency and electron transport rate were not affected by water stress up to 15 days after withholding water. Drought resistance of castor bean seems to be related to a pronounced, early growth response, an efficient stomatal control and the capacity to keep high net CO2 fixation rates under water stress conditions.

scholarly journals Effect of Irrigation and Soil Water Stress on Densities of Macrophomina phaseolina in Soil and Roots of Two Soybean Cultivars

Plant Disease ◽  
2000 ◽  
Vol 84 (8) ◽  
pp. 895-900 ◽  
Author(s):  
S. R. Kendig ◽  
J. C. Rupe ◽  
H. D. Scott
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Soil Water ◽  
Growth Stage ◽  
Root Colonization ◽  
Growing Season ◽  
Moisture Stress ◽  
Macrophomina Phaseolina ◽  
Soil Moisture Stress ◽  
Soil Water Stress

The effects of irrigation and soil water stress on Macrophomina phaseolina microsclerotial (MS) densities in the soil and roots of soybean were studied in 1988, 1989, and 1990. Soybean cvs. Davis and Lloyd received irrigation until flowering (TAR2), after flowering (IAR2), full season (FSI), or not at all (NI). Soil water matric potentials at 15- and 30-cm depths were recorded throughout the growing season and used to schedule irrigation. Soil MS densities were determined at the beginning of each season. Root MS densities were determined periodically throughout the growing season. Microsclerotia were present in the roots of irrigated as well as nonirrigated soybean within 6 weeks after planting. By vegetative growth stage V13, these densities reached relatively stable levels in the NI and FSI treatments (2.23 to 2.35 and 1.35 to 1.63 log [microsclerotia per gram of dry root], respectively) through reproductive growth stage R6. After R6, irrigation was discontinued and root densities of microsclerotia increased in all treatments. Initiation (IAR2) or termination (TAR2) of irrigation at R2 resulted in significant changes in root MS densities, with densities reaching levels intermediate between those of FSI and NI treatments. Year to year differences in root colonization reflected differences in soil moisture due to rainfall. The rate of root colonization in response to soil moisture stress decreased with plant age. Root colonization was significantly greater in Davis than Lloyd at R5 and R8. This was reflected in a trend toward higher soil densities of M. phaseolina at planting in plots planted with Davis than in plots planted with Lloyd. Although no charcoal rot symptoms in the plant were observed in this study, these results indicated that water management can limit, but not prevent, colonization of soybean by M. phaseolina, that cultivars differ in colonization, and that these differences may affect soil densities of the fungus.

Sign in / Sign up

Export Citation Format

Share Document