Rainfed and Supplemental Irrigation Modelling 2D GIS Moisture Rootzone Mapping on Yield and Seed Oil of Cotton (Gossypium hirsutum) Using Precision Agriculture and Remote Sensing

2021 ◽  
Vol 9 (1) ◽  
pp. 37
Author(s):  
Agathos Filintas ◽  
Aikaterini Nteskou ◽  
Persefoni Katsoulidi ◽  
Asimina Paraskebioti ◽  
Marina Parasidou
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Oil Content ◽  
Seed Oil ◽  
Substantial Improvement ◽  
Balance Model ◽  
Gis Mapping ◽  
Growth Stages ◽  
Supplemental Irrigation ◽  
Geostatistical Models

The effects of two irrigation (IR1: rainfed; IR2: rainfed + supplemental drip irrigation), and two fertilization (Ft1, Ft2) treatments were studied on cotton yield and seed oil by applying a number of new agro-technologies such as: TDR sensors; soil moisture (SM); precision agriculture; remote-sensing NDVI (Sentinel-2 satellite sensor); soil-hydraulic analyses; geostatistical models; SM-rootzone, and modelling 2D GIS mapping. A daily soil-water-crop-atmosphere (SWCA) balance model was developed. The two-way ANOVA statistical analysis results revealed that irrigation (IR2 = best) and fertilization treatments (Ft1 = best) significantly affected yield and oil content. Supplemental irrigation, if applied during critical growth stages, could result in substantial improvement on yield (+234.12%) and oil content (+126.44%).

scholarly journals Rainfed cultivation with supplemental irrigation modelling on seed yield and oil of Coriandrum sativum L. using precision agriculture and GIS moisture mapping

2021 ◽  
Author(s):  
Agathos Filintas ◽  
Eleni Wogiatzi ◽  
Nikolaos Gougoulias
Keyword(s):  
Soil Moisture ◽  
Essential Oil ◽  
Seed Yield ◽  
Precision Agriculture ◽  
Oil Content ◽  
Root Zone ◽  
Coriandrum Sativum ◽  
Gis Mapping ◽  
Growth Stages ◽  
Supplemental Irrigation

Abstract The aim of the present study was to determine the effects of rainfed and supplemental irrigation, and sowing period (SP) treatments on Coriander (Coriandrum sativum L.) yield, essential oil content and umbel heights by applying new agro-technologies (TDR-sensors for soil moisture (SM), GIS, Precision Agriculture, soil-hydraulic analyses and Geostatistical models) for yield and SM root zone geospatial modelling and two-dimensional GIS mapping. Results of laboratory analysis indicated a suitable soil for coriander's growth and revealed that field's soil was characterized Sandy Clay Loam(SCL) with mean values: Soil Organic Matter(SOM) = 1.70%, bulk specific gravity = 1.42 g·cm−3, Plant Available Water = 0.129 cm·cm−1, pH = 7.10 and cation-exchange capacity(CEC) = 19.3 cmol·kg−1. The two-way ANOVA statistical analysis (P = 0.05) results revealed that the irrigation treatments (IR1:rainfed, IR2:rainfed plus supplemental irrigation[best]), and the SP treatments (SP1:October's last week, SP2:November's first week[best]) significantly affects Coriander's seed yield and essential oil content, but the SP have no significant effect on plant's umbel height (P = 0.873). Supplemental irrigation, using a limited amount of water, if applied during the critical crop growth stages, can result in substantial improvement on seed yield (+284.934%), essential oil content (+125.396%) and plant's umbel height (+117.929%). HIGHLIGHT Geostatistical modelling on yield and oil of Coriander (Coriandrum sativum L.), GIS, Precision Agriculture, Rainfed cultivation with supplemental irrigation, Soil and hydraulic analyses, TDR-soil moisture mapping.

scholarly journals Soil Moisture Depletion Modelling Using a TDR Multi-Sensor System, GIS, Soil Analyzes, Precision Agriculture and Remote Sensing on Maize for Improved Irrigation-Fertilization Decisions

2021 ◽  
Vol 9 (1) ◽  
pp. 36
Author(s):  
Agathos Filintas
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Precision Agriculture ◽  
Maize Yield ◽  
Drainage Water ◽  
Substantial Improvement ◽  
Gis Mapping ◽  
Water Losses ◽  
Geostatistical Models ◽  
Sentinel 2

The effects of three drip irrigation (IR1: Farmer’s, IR2:Full (100%ETc), IR3:Deficit (80%ETc) irrigation), and two fertilization (Ft1, Ft2) treatments were studied on maize yield and biomass by applying new agro-technologies (TDR—sensors for soil moisture (SM) measurements, Precision Agriculture, Remote Sensing—NDVI (Sentinel-2 satellite sensor), soil-hydraulic analyses and Geostatistical models, SM-rootzone modelling-2D-GIS mapping). A daily soil moisture depletion (SMDp) model was developed. The two-way-ANOVA statistical analysis results revealed that irrigation (IR3 = best) and fertilization treatments (Ft1 = best) significantly affect yield and biomass. Deficit irrigation and proper fertilization based on new agro-technologies for improved management decisions can result in substantial improvement on yield (+116.10%) and biomass (+119.71%) with less net water use (−7.49%) and reduced drainage water losses (−41.02%).

Remote Sensing of Evapotranspiration Using SEBAL and Metric Energy Balance Models for Enhanced Precision Agriculture Cotton Irrigation Scheduling

2020 ◽  
Author(s):  
Francesco Morari ◽  
Ahmed Harb Rabia ◽  
Stefano Lo Presti ◽  
Stefano Gobbo ◽  
George Vellidis
Keyword(s):  
Remote Sensing ◽  
Energy Balance ◽  
Precision Agriculture ◽  
Irrigation Schedule ◽  
Plant Cover ◽  
Crop Coefficient ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Balance Model ◽  
Scheduling Irrigation

<p>Irrigation scheduling is one of the main factors that affect the crop ability to resist stress symptoms in addition to affecting directly the final yield. In the last decade, many remote sensing methods have been developed to help in scheduling irrigation with higher precision. Some of these methods estimate irrigation needs indirectly such as those using normalized difference vegetation index (NDVI) or crop coefficient curve, and other methods that directly calculate Evapotranspiration (ET) through satellite images. Cotton SmartIrrigation App (Cotton App) is one of the recent applications that have been developed to help farmers in scheduling irrigation during the growing season. The App is based on an interactive ET-based soil water balance model. In this study, remote sensing of Evapotranspiration has been used to detect and map crop water requirements in order to enhance the Cotton App predictions for irrigation schedule during the growing season. Two remote sensing ET models based on thermal infrared (TIR), The surface energy balance algorithm for land (SEBAL) and Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were used to derive ET over cotton. Results showed higher values of actual Evapotranspiration calculated by both SEBAL and METRIC models during the first 45 days of the growing season compared to the calculated values of ETa from crop coefficient. This is expected to be due to the higher evaporation fraction from bare soil since the plant cover is still very low and accordingly the plant transpiration too. However, later in the second growing stage, the models showed that the crop coefficient calculated ETa (ETa- Calculated) has overestimated the plant Evapotranspiration giving higher values compared to the values from the models. These results indicate that, the use of remote sensing techniques along with the ET-models will increase the app efficiency in giving more precise irrigation scheduling.</p>

scholarly journals Comparison of Remote Sensing based Multi-Source ET Models over Cropland in a Semi-Humid Region of China

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 325
Author(s):  
Qifeng Zhuang ◽  
Hao Wang ◽  
Yuqi Xu
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Remote Sensing Data ◽  
Moisture Stress ◽  
Balance Model ◽  
Maturation Stage ◽  
Growth Stages ◽  
Landsat 8 ◽  
Humid Region ◽  
Crop Types

The estimation of cropland evapotranspiration (ET) is essential for agriculture water management, drought monitoring, and yield forecast. Remote sensing-based multi-source ET models have been widely applied and validated in the semi-arid region of China. However, careful investigation of the models’ performances for different crop types (winter wheat and summer maize) over the semi-humid region is still necessary. This study used remote sensing data (Landsat 8 and ASTER) and compared three mainstream multi-source ET models: (i) the two-source energy balance model, i.e., TSEB; (ii) the Penman-Monteith based four-source model, i.e., 4s-PM; (iii) the Priestley Taylor-Jet Propulsion Laboratory ET algorithm, i.e., PT-JPL. The measurements of the eddy-covariance (EC) flux tower located in Guantao county of North China were used to validate the models. The results showed that the TSEB model performed the best in estimating latent heat flux (LE) of maize, with an RMSE of 75.0 W/m2 and an R2 of 0.9, and the 4s-PM model had the highest accuracy of LE estimation for wheat, with an RMSE of 61.0 W/m2 and an R2 of 0.91. The LE spatial distribution comparison indicated that the PT-JPL model had more capacity to exhibit crop ET heterogeneity. The major environmental factors affecting ET varied with crop types and crop growth stages. Without taking soil moisture into account, the 4s-PM and TSEB models overestimated LE under water deficit in the maturation stage of wheat. The plant moisture stress based on vegetation index in the PT-JPL model underestimated the evaporation in the maturation stage while the cropland was still wet.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Registration of ‘NC-Miller’ Soybean with High Yield and High Seed-Oil Content

2012 ◽  
Vol 6 (3) ◽  
pp. 294-297 ◽  
Author(s):  
J. W. Burton ◽  
L. M. Miranda ◽  
T. E. Carter ◽  
D. T. Bowman
Keyword(s):  
Oil Content ◽  
Seed Oil ◽  
High Yield ◽  
Seed Oil Content

scholarly journals Unravelling the Complex Interplay of Transcription Factors Orchestrating Seed Oil Content in Brassica napus L.

2021 ◽  
Vol 22 (3) ◽  
pp. 1033
Author(s):  
Abirami Rajavel ◽  
Selina Klees ◽  
Johanna-Sophie Schlüter ◽  
Hendrik Bertram ◽  
Kun Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Brassica Napus ◽  
Oil Content ◽  
Seed Oil ◽  
Seed Oil Content ◽  
Complex Interplay ◽  
Brassica Napus L ◽  
Data Set ◽  
Developmental Switches

Transcription factors (TFs) and their complex interplay are essential for directing specific genetic programs, such as responses to environmental stresses, tissue development, or cell differentiation by regulating gene expression. Knowledge regarding TF–TF cooperations could be promising in gaining insight into the developmental switches between the cultivars of Brassica napus L., namely Zhongshuang11 (ZS11), a double-low accession with high-oil- content, and Zhongyou821 (ZY821), a double-high accession with low-oil-content. In this regard, we analysed a time series RNA-seq data set of seed tissue from both of the cultivars by mainly focusing on the monotonically expressed genes (MEGs). The consideration of the MEGs enables the capturing of multi-stage progression processes that are orchestrated by the cooperative TFs and, thus, facilitates the understanding of the molecular mechanisms determining seed oil content. Our findings show that TF families, such as NAC, MYB, DOF, GATA, and HD-ZIP are highly involved in the seed developmental process. Particularly, their preferential partner choices as well as changes in their gene expression profiles seem to be strongly associated with the differentiation of the oil content between the two cultivars. These findings are essential in enhancing our understanding of the genetic programs in both cultivars and developing novel hypotheses for further experimental studies.

scholarly journals Development and screening of EMS mutants with altered seed oil content or fatty acid composition in Brassica napus

2020 ◽  
Vol 104 (5) ◽  
pp. 1410-1422
Author(s):  
Shan Tang ◽  
Dong‐Xu Liu ◽  
Shaoping Lu ◽  
Liangqian Yu ◽  
Yuqing Li ◽  
...  
Keyword(s):  
Fatty Acid Composition ◽  
Fatty Acid ◽  
Brassica Napus ◽  
Acid Composition ◽  
Oil Content ◽  
Seed Oil ◽  
Seed Oil Content
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