Functional soil mapping for site-specific soil moisture and crop yield management

Geoderma ◽  
2013 ◽  
Vol 200-201 ◽  
pp. 45-54 ◽  
Author(s):  
Q. Zhu ◽  
H.S. Lin ◽  
J.A. Doolittle
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Yield Management ◽  
Soil Mapping ◽  
Site Specific

scholarly journals The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

2021 ◽  
Vol 13 (4) ◽  
pp. 2362
Author(s):  
Thomas M. Koutsos ◽  
Georgios C. Menexes ◽  
Andreas P. Mamolos
Keyword(s):  
Decision Making ◽  
Spatial Analysis ◽  
Crop Yield ◽  
Spatial Autocorrelation Analysis ◽  
Autocorrelation Analysis ◽  
Yield Data ◽  
Site Specific ◽  
Production Areas ◽  
Short Time ◽  
Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

scholarly journals Probabilistic inference of ecohydrological parameters using observations from point to satellite scales

2018 ◽  
Vol 22 (6) ◽  
pp. 3229-3243 ◽  
Author(s):  
Maoya Bassiouni ◽  
Chad W. Higgins ◽  
Christopher J. Still ◽  
Stephen P. Good
Keyword(s):  
Soil Moisture ◽  
Water Balance ◽  
Soil Water ◽  
Dry Season ◽  
Soil Water Balance ◽  
Rainfall Pattern ◽  
Site Specific ◽  
Soil Moisture Dynamics ◽  
Soil Saturation ◽  
Moisture Dynamics

Abstract. Vegetation controls on soil moisture dynamics are challenging to measure and translate into scale- and site-specific ecohydrological parameters for simple soil water balance models. We hypothesize that empirical probability density functions (pdfs) of relative soil moisture or soil saturation encode sufficient information to determine these ecohydrological parameters. Further, these parameters can be estimated through inverse modeling of the analytical equation for soil saturation pdfs, derived from the commonly used stochastic soil water balance framework. We developed a generalizable Bayesian inference framework to estimate ecohydrological parameters consistent with empirical soil saturation pdfs derived from observations at point, footprint, and satellite scales. We applied the inference method to four sites with different land cover and climate assuming (i) an annual rainfall pattern and (ii) a wet season rainfall pattern with a dry season of negligible rainfall. The Nash–Sutcliffe efficiencies of the analytical model's fit to soil observations ranged from 0.89 to 0.99. The coefficient of variation of posterior parameter distributions ranged from < 1 to 15 %. The parameter identifiability was not significantly improved in the more complex seasonal model; however, small differences in parameter values indicate that the annual model may have absorbed dry season dynamics. Parameter estimates were most constrained for scales and locations at which soil water dynamics are more sensitive to the fitted ecohydrological parameters of interest. In these cases, model inversion converged more slowly but ultimately provided better goodness of fit and lower uncertainty. Results were robust using as few as 100 daily observations randomly sampled from the full records, demonstrating the advantage of analyzing soil saturation pdfs instead of time series to estimate ecohydrological parameters from sparse records. Our work combines modeling and empirical approaches in ecohydrology and provides a simple framework to obtain scale- and site-specific analytical descriptions of soil moisture dynamics consistent with soil moisture observations.

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Geoderma ◽  
2019 ◽  
Vol 337 ◽  
pp. 607-621 ◽  
Author(s):  
Sanne Diek ◽  
Sabine Chabrillat ◽  
Marco Nocita ◽  
Michael E. Schaepman ◽  
Rogier de Jong
Keyword(s):  
Soil Moisture ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Imaging Spectrometer ◽  
Multi Temporal ◽  
Airborne Imaging ◽  
Spectrometer Data

scholarly journals Crop Cultivation in a Greenhouse using IOT

2021 ◽  
Vol 9 (VI) ◽  
pp. 4432-4437
Author(s):  
K. Akanksha
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Mobile Phones ◽  
Temperature Sensor ◽  
Moisture Level ◽  
Crop Cultivation ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
Turn On ◽  
Light Sensor

Nowadays gardening has become a hobby for everyone. Everyone is showing interest in growing their own plants in their houses like terrace farming. So we have decided to do a project which can be useful for everyone even the farmers can be benefitted by our project. In our project we are preparing a greenhouse for cultivating different kinds of crops. Our greenhouse consists of arduino UNO, sensors like (temperature sensor, soil moisture sensor, colour sensor and light sensor), actuators. All these are used in sensing the outside environment and giving signals to arduino so that it sends the signal through GSM module and this GSM module will give us a message alert through our mobile phones like for example if the moisture is less in soil then we will get alert “your moisture has decreased water the plants” so that we can turn on our motor pumps to water. Here we are using thingspeak cloud for coding the arduino through IOT. Our project will also do its watering by itself when the moisture level decreases, this is done by soil moisture sensor. It is very reasonable and complete greenhouse can be constructed under Rs.10,000 which can save lots of money for the farmers. The crop yield will also be very good and this will be useful in increasing the economy of farming.

A COMPARISON OF SOME TILLAGE METHODS FOR CORN ON BROOKSTON CLAY SOIL

1957 ◽  
Vol 37 (2) ◽  
pp. 113-119
Author(s):  
E. F. Bolton ◽  
J. W. Aylesworth
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Clay Soil ◽  
Corn Yield ◽  
Corn Production ◽  
Moisture Supply ◽  
Second Year ◽  
Tillage Method ◽  
Tillage Methods

Methods of mulch-planting were compared with mouldboard-ploughing methods for corn production at Woodslee during 1953, 1954 and 1955. The tillage treatments were established in second-year alfalfa sod on Brookston clay soil. On the basis of corn yield the conventional ploughing treatments were greatly superior to any mulch-plant method tested during the three years. Soil moisture studies indicated that the effect of the intercrop on the soil moisture supply was the major factor influencing crop yield, but moisture alone did not account entirely for the differences obtained in crop yield. The plough-plant method produced as good corn yields as spring ploughing in 1953 but somewhat less in 1954 and 1955. The results would suggest that an adaptation of the plough-plant method may have possible application as a tillage method for corn on the finer textured soils of southwestern Ontario.

Hydrologic Downscaling of Soil Moisture Using Global Data Sets without Site-Specific Calibration

2018 ◽  
Vol 23 (11) ◽  
pp. 04018048 ◽  
Author(s):  
Nicholas R. Grieco ◽  
Jeffrey D. Niemann ◽  
Timothy R. Green ◽  
Andrew S. Jones ◽  
Peter J. Grazaitis
Keyword(s):  
Soil Moisture ◽  
Data Sets ◽  
Site Specific ◽  
Global Data Sets ◽  
Global Data

scholarly journals Investigating temporal field sampling strategies for site-specific calibration of three soil moisture–neutron intensity parameterisation methods

2015 ◽  
Vol 19 (7) ◽  
pp. 3203-3216 ◽  
Author(s):  
J. Iwema ◽  
R. Rosolem ◽  
R. Baatz ◽  
T. Wagener ◽  
H. R. Bogena
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Cosmic Ray ◽  
Sampling Strategies ◽  
Site Specific ◽  
Neutron Intensity ◽  
Soil Wetness ◽  
Semi Arid

Abstract. The Cosmic-Ray Neutron Sensor (CRNS) can provide soil moisture information at scales relevant to hydrometeorological modelling applications. Site-specific calibration is needed to translate CRNS neutron intensities into sensor footprint average soil moisture contents. We investigated temporal sampling strategies for calibration of three CRNS parameterisations (modified N0, HMF, and COSMIC) by assessing the effects of the number of sampling days and soil wetness conditions on the performance of the calibration results while investigating actual neutron intensity measurements, for three sites with distinct climate and land use: a semi-arid site, a temperate grassland, and a temperate forest. When calibrated with 1 year of data, both COSMIC and the modified N0 method performed better than HMF. The performance of COSMIC was remarkably good at the semi-arid site in the USA, while the N0mod performed best at the two temperate sites in Germany. The successful performance of COSMIC at all three sites can be attributed to the benefits of explicitly resolving individual soil layers (which is not accounted for in the other two parameterisations). To better calibrate these parameterisations, we recommend in situ soil sampled to be collected on more than a single day. However, little improvement is observed for sampling on more than 6 days. At the semi-arid site, the N0mod method was calibrated better under site-specific average wetness conditions, whereas HMF and COSMIC were calibrated better under drier conditions. Average soil wetness condition gave better calibration results at the two humid sites. The calibration results for the HMF method were better when calibrated with combinations of days with similar soil wetness conditions, opposed to N0mod and COSMIC, which profited from using days with distinct wetness conditions. Errors in actual neutron intensities were translated to average errors specifically to each site. At the semi-arid site, these errors were below the typical measurement uncertainties from in situ point-scale sensors and satellite remote sensing products. Nevertheless, at the two humid sites, reduction in uncertainty with increasing sampling days only reached typical errors associated with satellite remote sensing products. The outcomes of this study can be used by researchers as a CRNS calibration strategy guideline.

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.

Usage of soil moisture models in agronomic research

1996 ◽  
Vol 76 (3) ◽  
pp. 285-295 ◽  
Author(s):  
O. O. Akinremi ◽  
S. M. McGinn
Keyword(s):  
Soil Moisture ◽  
Grain Yield ◽  
Water Use ◽  
Crop Yield ◽  
Drought Index ◽  
Regional Scale ◽  
Irrigation Scheduling ◽  
Water Model ◽  
Yield Reduction ◽  
Efficiency Coefficient

Soil moisture controls many important processes in the soil-plant system and the extent of these processes cannot be quantified without knowing moisture status of the root zone. Of agronomic importance these include, seedling emergence, evapotranspiration, mineralization of the soil organic fraction, surface runoff, leaching and crop yield. Many models have been developed to simulate these processes based on algorithms of varying degrees of complexity that describe the dynamic nature of soil moisture at different temporal and spatial scales. This paper reviews the direct applications of soil moisture models in agronomy from the field to regional scale and for daily to seasonal time steps. At every level of detail, the lack of model validation beyond the region where it was developed is the main limitation to the application of soil moisture models in agronomy. At the field scale, models have been used for irrigation scheduling to ensure efficient utilization of irrigation water and maximize crop yields. Models are also used to estimate crop yield based on the growing season water use. The water use of crops is converted to biomass accumulation and grain yield using a water-use efficiency coefficient and a harvest index. Other empirical equations are available that relate cumulative crop water use directly to grain yield. On a regional scale, in a study of drought climatology on the Canadian prairie, we coupled a soil water model, the Versatile Soil Moisture Budget, with the Palmer Drought Index model to improve the modelling of soil moisture. This was found to improve the relationship of the Palmer drought index to wheat yield reduction resulting from drought. Key words: Soil moisture, modelling, water-use, evapotranspiration, aridity index, Canadian prairies

The relationship between extreme weather and low crop yields

2020 ◽  
Author(s):  
Matias Heino ◽  
Weston Anderson ◽  
Michael Puma ◽  
Matti Kummu
Keyword(s):  
Soil Moisture ◽  
Crop Yield ◽  
Crop Yields ◽  
Explanatory Power ◽  
The United States ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Data ◽  
Yield Variability ◽  
Weather Events

&lt;p&gt;It is well known that climate extremes and variability have strong implications for crop productivity. Previous research has estimated that annual weather conditions explain a third of global crop yield variability, with explanatory power above 50% in several important crop producing regions. Further, compared to average conditions, extreme events contribute a major fraction of weather induced crop yield variations. Here we aim to analyse how extreme weather events are related to the likelihood of very low crop yields at the global scale. We investigate not only the impacts of heat and drought on crop yields but also excess soil moisture and abnormally cool temperatures, as these extremes can be detrimental to crops as well. In this study, we combine reanalysis weather data with national and sub-national crop production statistics and assess relationships using statistical copulas methods, which are especially suitable for analysing extremes. Further, because irrigation can decrease crop yield variability, we assess how the observed signals differ in irrigated and rainfed cropping systems. We also analyse whether the strength of the observed statistical relationships could be explained by socio-economic factors, such as GDP, social stability, and poverty rates. Our preliminary results indicate that extreme heat and cold as well as soil moisture abundance and excess have a noticeable effect on crop yields in many areas around the globe, including several global bread baskets such as the United States and Australia. This study will increase understanding of extreme weather-related implications on global food production, which is relevant also in the context of climate change, as the frequency of extreme weather events is likely to increase in many regions worldwide.&lt;/p&gt;

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