scholarly journals Agro-climatic onset of cropping season: A tool for determining optimum date of sowing in dry zones of southern Karnataka

2021 ◽  
Vol 22 (3) ◽  
pp. 240-249
Author(s):  
LINGARAJ HUGGI ◽  
H.S. SHIVARAMU ◽  
M.H. MANJUNATAHA ◽  
D.V. SOUMYA ◽  
P. VIJAYA KUMAR ◽  
...  
Keyword(s):  
Crop Production ◽  
Finger Millet ◽  
Bimodal Distribution ◽  
Temporal Variations ◽  
Cropping Season ◽  
Dry Spell ◽  
Dry Zone ◽  
Threshold Rainfall ◽  
Spatio Temporal ◽  
Dry Farming

The study was conducted to analyse the rainfall pattern of dry farming zones of Southern Karnataka to arrive at proper date of sowing by considering parameters like threshold rainfall (20 mm), threshold dry day (2.5 mm) and threshold dry spell period (10 days) as a main defining parameters for decision making in sowing of major crops (finger millet, pigeonpea, groundnut, etc.). In all the three zones, the agro-climatic onset of cropping season was earlier as compared to meteorological onset (June 1st week) due to bimodal distribution of rainfall having its peaks in May and September month. In Central Dry Zone, Southern Dry Zone and Eastern Dry Zone, fourteenth June, thirteenth June and twentythird May were the agro-climatic onset dates (average of all stations in each zone), respectively. Station wise analysis of the rainfall revealed different agro-climatic onset dates. Ninth May in central dry zone, eighth May in eastern dry zone and fifth May in southern dry zone were the earliest onset dates. These variations in between zonal and station specific onset dates were due to spatio-temporal variations in rainfall. Therefore, advancements in sowing of crops based on the agro-climatic onset should be taken into account for betterment of crop production.

scholarly journals Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

2020 ◽  
Vol 21 (6) ◽  
pp. 1263-1290
Author(s):  
Gerald Blasch ◽  
Zhenhai Li ◽  
James A. Taylor
Keyword(s):  
Crop Production ◽  
Pattern Analysis ◽  
Production Systems ◽  
Principal Component ◽  
Temporal Variations ◽  
Data Sets ◽  
Yield Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Cereal Fields

Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

Spatio-temporal variations of winter wheat water requirement and climatic causes in Huang-Huai-Hai Farming Region

2012 ◽  
Vol 20 (3) ◽  
pp. 356-362 ◽  
Author(s):  
Xiao-Lin YANG ◽  
Zhen-Wei SONG ◽  
Hong WANG ◽  
Quan-Hong SHI ◽  
Fu CHEN ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Temporal Variations ◽  
Water Requirement ◽  
Spatio Temporal

ASSESSMENT OF THE SPATIO-TEMPORAL VARIATIONS IN GROUND WATER STORAGE USING DOWN-SCALED GRACE DATA

2018 ◽  
Author(s):  
Hossein Sahour ◽  
◽  
Mohamed Sultan ◽  
Karem Abdelmohsen ◽  
Sita Karki ◽  
...  
Keyword(s):  
Ground Water ◽  
Water Storage ◽  
Temporal Variations ◽  
Grace Data ◽  
Spatio Temporal

scholarly journals A robust machine learning approach to SDG data segmentation

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Kassim S. Mwitondi ◽  
Isaac Munyakazi ◽  
Barnabas N. Gatsheni
Keyword(s):  
Temporal Variations ◽  
Data Repository ◽  
The Sustainable Development ◽  
Complex Interactions ◽  
Data Segmentation ◽  
Technological Advances ◽  
Machine Learning Approach ◽  
Development Goals ◽  
Data Source ◽  
Spatio Temporal

Abstract In the light of the recent technological advances in computing and data explosion, the complex interactions of the Sustainable Development Goals (SDG) present both a challenge and an opportunity to researchers and decision makers across fields and sectors. The deep and wide socio-economic, cultural and technological variations across the globe entail a unified understanding of the SDG project. The complexity of SDGs interactions and the dynamics through their indicators align naturally to technical and application specifics that require interdisciplinary solutions. We present a consilient approach to expounding triggers of SDG indicators. Illustrated through data segmentation, it is designed to unify our understanding of the complex overlap of the SDGs by utilising data from different sources. The paper treats each SDG as a Big Data source node, with the potential to contribute towards a unified understanding of applications across the SDG spectrum. Data for five SDGs was extracted from the United Nations SDG indicators data repository and used to model spatio-temporal variations in search of robust and consilient scientific solutions. Based on a number of pre-determined assumptions on socio-economic and geo-political variations, the data is subjected to sequential analyses, exploring distributional behaviour, component extraction and clustering. All three methods exhibit pronounced variations across samples, with initial distributional and data segmentation patterns isolating South Africa from the remaining five countries. Data randomness is dealt with via a specially developed algorithm for sampling, measuring and assessing, based on repeated samples of different sizes. Results exhibit consistent variations across samples, based on socio-economic, cultural and geo-political variations entailing a unified understanding, across disciplines and sectors. The findings highlight novel paths towards attaining informative patterns for a unified understanding of the triggers of SDG indicators and open new paths to interdisciplinary research.

Spatio-temporal variations of sea surface halocarbon concentrations and fluxes from southern Yellow Sea

2014 ◽  
Vol 121 (2) ◽  
pp. 369-388 ◽  
Author(s):  
Gui-Peng Yang ◽  
Bin Yang ◽  
Xiao-Lan Lu ◽  
Hai-Bing Ding ◽  
Zhen He
Keyword(s):  
Yellow Sea ◽  
Temporal Variations ◽  
Sea Surface ◽  
Southern Yellow Sea ◽  
Spatio Temporal
Sign in / Sign up

Export Citation Format

Share Document