Wheat growth profile: satellite monitoring and crop yield modelling

2004 ◽  
Vol 32 (1) ◽  
pp. 91-102 ◽  
Author(s):  
K. R. Manjunath ◽  
M. B. Potdar
Keyword(s):  
Crop Yield ◽  
Satellite Monitoring ◽  
Wheat Growth ◽  
Growth Profile

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

Spectral wheat growth profile in Punjab using IRS WiFS data

2005 ◽  
Vol 33 (2) ◽  
pp. 345-352 ◽  
Author(s):  
D. R. Rajak ◽  
M. P. Oza ◽  
N. Bhagiaand ◽  
V. K. Dadhwal
Keyword(s):  
Wheat Growth ◽  
Growth Profile ◽  
Wifs Data

Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data

2003 ◽  
Vol 24 (10) ◽  
pp. 2037-2054 ◽  
Author(s):  
M. H. Kalubarme ◽  
M. B. Potdar ◽  
K. R. Manjunath ◽  
R. K. Mahey ◽  
S. S. Siddhu
Keyword(s):  
Crop Yield ◽  
Wheat Yield ◽  
Large Area ◽  
Growth Profile ◽  
Noaa Avhrr ◽  
Avhrr Data

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

Evaluation of nitrogen dynamics and crop yield in a long-term tillage experiment

2007 ◽  
Vol 35 (2) ◽  
pp. 769-772 ◽  
Author(s):  
Attila Megyes ◽  
Tamás Rátonyi ◽  
Dénes Sulyok
Keyword(s):  
Crop Yield ◽  
Nitrogen Dynamics
Sign in / Sign up

Export Citation Format

Share Document