scholarly journals Coffee yield forecasting using climate indices based agrometeorological model in Kerala

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 309-316
Author(s):  
M. JAYAKUMAR ◽  
M. RAJAVEL
Keyword(s):  
Weather Data ◽  
Climate Indices ◽  
Research Station ◽  
Statistical Regression ◽  
Climate Data ◽  
Robusta Coffee ◽  
Coffee Yield ◽  
Kerala State ◽  
Term Data

Climate plays important role in production of coffee. Adequate quantum and timely receipt of blossom rainfall for flowering and subsequent backing showers influence the berry set and yield of coffee. Harvesting of Arabica coffee in Kerala State with humid tropical climate in India is done by December-January and harvesting of Robusta coffee is taken up during January-February. In this paper, attempt was made to develop agrometeorological models to forecast the yield of these two varieties coffee by utilising monthly climate variables from January to December. Long term data from 1991-92 to 2012-13 on coffee yield and weather data from 1991-2012 recorded at Regional Coffee Research Station, Chundale located in Wayanad district of Kerala State was used to develop agrometeorological model. Statistical regression model between climate indices and yield of Arabica and Robusta coffee was developed and the model was validated using crop and climate data for 2013 and 2014. The model demonstrated that climate indices based agrometeological model is able to forecast the yield of coffee in Kerala.  

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Weather based forewarning models for coffee berry borer and shot hole borer in Wayanad, Kerala

MAUSAM ◽  
2021 ◽  
Vol 67 (4) ◽  
pp. 879-886
Author(s):  
M. JAYAKUMAR ◽  
C. K. VIJAYALAKSHMI ◽  
P. ABDUL RAHIMAN ◽  
M. RAJAVEL
Keyword(s):  
Maximum Temperature ◽  
Research Station ◽  
Coffee Berry ◽  
Weather Variables ◽  
Coffee Berry Borer ◽  
Coffee Plantations ◽  
Pest Damage ◽  
Weather Parameters ◽  
Term Data

Pest damage due to coffee berry borer and shot hole borer in coffee plantations in Regional Coffee Research Station, Chundale and data on weather parameters were recorded during 1977 to 2007 (30 years). These long- term data on the pest damage and weather parameters were utilized to study the influence of weather variables on coffee berry and shot hole borer incidence with a view to develop weather based forewarning models for coffee berry borer and shot hole borer damage in Wayanad. The damage of coffee berry borer (CBB) was observed to be significant during January to March while the damage of shot hole borer (SHB) was spread during January to April and October to December. Highest percent damage of coffee berry borer and shot hole borer was observed during first fortnight of January. Maximum damage due to coffee berry borer was observed during 1982 and maximum damage due to shot hole borer was observed in 1994. Maximum temperature recorded during the first fortnight of January is predominant weather variable determining infestation of shot hole borer during first fortnight of January. Harvest and budding stages of the crop suffered heavy incidence of coffee berry borer and shot hole borer, respectively. 

An empirical approach to the analysis of local and global climate and weather data and to the determination of CO2 sensitivities

2021 ◽  
Author(s):  
Wolf Timm
Keyword(s):  
Global Climate ◽  
Global Temperature ◽  
Weather Data ◽  
Data Sets ◽  
Climate Data ◽  
Local Data ◽  
Simple Method ◽  
Co2 Sensitivity ◽  
Data Points

Abstract Some freely available global temperature data sets which document the weather for a period of over 100 years, e.g. from NASA, from NOAA, additionally also local data e.g. for Germany (DWD) were analyzed in order to derive meaningful empirical long-term trends with suitable multi-annual averages. This is first demonstrated using global climate data with different approaches, whereby the results are to a high degree consistent. Analyzes of the German temperature and weather data and of climate data from other continents are carried out in a similar manner. For reliable forecasts it is important to determine the CO2 sensitivity as precisely as possible. A very simple method is to smooth out temperatures over 20 years at a time. If these values are plotted at intervals of 10 years over the associated (also averaged) CO2 content, the temperature database (since 1961) is condensed to 5 data points and a statement can be made about the quality of the linearity for the respective database. Both the NASA data and the NOAA data show an unusually good linearity with almost identical CO2 sensitivity (approx. 0.0105 K/ppm CO2). This indicates that the long-term trend in global temperature since around 1960 has been largely determined solely by greenhouse gases. If the regional weather data is used as a basis, there is also in many cases strict linearity with increasing CO2 content. The analysis of the regional data allows the conclusion that there is approximately a specific CO2 sensitivity for every region on earth with specific statistical uncertainties: For mean global land, it is 0.017 K, for Germany it is 0.022 K, and for Alaska even 0.028 K per ppm CO2 .

scholarly journals Climate Index Metadata and its Implementation

2020 ◽  
Author(s):  
Klaus Zimmermann ◽  
Lars Bärring
Keyword(s):  
High Performance ◽  
Large Data ◽  
Climate Science ◽  
Weather Data ◽  
Climate Index ◽  
Climate Indices ◽  
Climate Data ◽  
Efficient Treatment ◽  
Novel Approach ◽  
Wide Range

<p>Climate indices play an important role in the practical use of climate and weather data. Their application spans a wide range of topics, from impact assessment in agriculture and urban planning, over indispensable advice in the energy sector, to important evaluation in the climate science community. Several widely used standard sets of indices exist through long-standing efforts of WMO and WCRP Expert Teams (ETCCDI and ET-SCI), as well as European initiatives (ECA&D) and more recently Copernicus C3S activities. They, however, focus on the data themselves, leaving much of the metadata to the individual user. Moreover, these core sets of indices lack a coherent metadata framework that would allow for the consistent inclusion of new indices that continue to be considered every day.</p><p>In the meantime, the treatment of metadata in the wider community has received much attention. Within the climate community efforts such as the CF convention and the much-expanded scope and detail of metadata in CMIP6 have improved the clarity and long-term usability of many aspects of climate data a great deal.</p><p>We present a novel approach to metadata for climate indices. Our format describes the existing climate indices consistent with the established standards, adding metadata along the lines of existing metadata specifications. The formulation of these additions in a coherent framework encompassing most of the existing climate index standards allows for its easy extension and inclusion of new climate indices as they are developed.</p><p>We also present Climix, a new Python software for the calculation of indices based on this description. It can be seen as an example implementation of the proposed standard and features high-performance calculations based on state-of-the-art infrastructure, such as Iris and Dask. This way, it offers shared memory and distributed parallel and out-of-core computations, enabling the efficient treatment of large data volumes as incurred by the high resolution, long time-series of current and future datasets.</p>

Finding and analysing long-term climate data

2019 ◽  
pp. 29-36
Author(s):  
Mark D. Schwartz ◽  
Liang Liang
Keyword(s):  
Growth And Development ◽  
Vegetation Change ◽  
Spatial Scales ◽  
Weather Data ◽  
Terrestrial Vegetation ◽  
Climate Data ◽  
Related Data ◽  
The Impact ◽  
Derived Data

This chapter provides an overview of major sources of long-term climate and related data and gives some guidance and recommendations to help select the best data for specific research projects. Major long-term climate datasets are either station-based or gridded arrays (at different spatial resolutions). Terrestrial vegetation change can be monitored in real-time using satellite-derived data. Software tools have been developed that facilitate convenient retrieval and use of climate data for ecological research. Phenological models process weather data into indices directly related to growth and development of many plant species. A crucial consideration in any analysis of the relationships among climate data and biological activity is the period of influence, and downscaling can bridge spatial scales. Measurement issues, the impact of means or extremes, as well as variations in scale and time, are all important when pondering the best climate data for a specific study.

scholarly journals Generation of a Test Reference Year for Alūksne, Latvia

2015 ◽  
Vol 33 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Mārtiņš Ruduks ◽  
Arturs Lešinskis
Keyword(s):  
Selection Process ◽  
Meteorological Data ◽  
Weather Conditions ◽  
Building Energy ◽  
Weather Data ◽  
Building Performance ◽  
Climate Data ◽  
Meteorological Observations ◽  
Energy Simulations

Abstract Precise and reliable meteorological data are necessary for building performance analysis. Since meteorological conditions vary significantly from year to year, there is a need to create a test reference year (TRY), to represent the long-term weather conditions over a year. In this paper two different TRY data models were generated and compared: TRY and TRY-2. Both models where created by analysing every 3-hour weather data for a 30-year period (1984–2013) in Alūksne, Latvia, provided by the Latvian Environment Geology and Meteorology Centre (LEGMC). TRY model was generated according to standard LVS EN ISO 15927-4, but to create second model - TRY-2, 30 year average data were applied. The generated TRY contains typical months from a number of different years. The data gathered from TRY and TRY-2 models where compared with the climate data from the Latvian Cabinet of Ministers regulation No. 379, Regulations Regarding Latvian Building Code LBN 003-01. Average monthly temperature values in LBN 003-01 were lower than the TRY and TRY-2 values. The results of this study may be used in building energy simulations and heating-cooling load calculations for selected region. TRY selection process should include the most recent meteorological observations and should be periodically renewed to reflect the long-term climate change.

Hydrogeological responses in tropical mountainous springs

2019 ◽  
Author(s):  
Ricardo Sánchez-Murillo
Keyword(s):  
Costa Rica ◽  
Isotope Composition ◽  
Central Valley ◽  
Distribution Functions ◽  
Geochemical Composition ◽  
Transit Times ◽  
Management Plans ◽  
Best Fit ◽  
Term Data

This study presents a hydrogeochemical analysis of spring responses (2013-2017) in the tropical mountainous region of the Central Valley of Costa Rica. The isotopic distribution of δ18O and δ2H in rainfall resulted in a highly significant meteoric water line: δ2H = 7.93×δ18O + 10.37 (r2=0.97). Rainfall isotope composition exhibited a strong dependent seasonality. The isotopic variation (δ18O) of two springs within the Barva aquifer was simulated using the FlowPC program to determine mean transit times (MTTs). Exponential-piston and dispersion distribution functions provided the best-fit to the observed isotopic composition at Flores and Sacramento springs, respectively. MTTs corresponded to 1.23±0.03 (Sacramento) and 1.42±0.04 (Flores) years. The greater MTT was represented by a homogeneous geochemical composition at Flores, whereas the smaller MTT at Sacramento is reflected in a more variable geochemical response. The results may be used to enhance modelling efforts in central Costa Rica, whereby scarcity of long-term data limits water resources management plans.

Morphometric studies of artemia males from different populations of the Altai region

2020 ◽  
pp. 28-38
Author(s):  
L. Vesnina ◽  
G. Lukerina ◽  
T. Ronzhina ◽  
A. Savos’kin ◽  
D. Surkov
Keyword(s):  
Sexual Dimorphism ◽  
Hydrogen Index ◽  
Morphometric Parameters ◽  
Altai Region ◽  
Sexual Structure ◽  
Different Populations ◽  
Artemia Population ◽  
Term Data ◽  
Brine Salinity

The long-term data from morphometric studies of Artemia males from bisexual and parthenogenetic populations from hyperhaline reservoirs of the Altai region (Bolshoe Yarovoe Lake, Maloe Shklo Lake, and the Tanatar Lakes system) is analyzed in this paper. The description of signs of sexual dimorphism and sexual structure in different populations is given. The influence of brine salinity and hydrogen index on morphometric parameters of males was analyzed. There are differences in the sexual structure of the Artemia population: in the lakes Maloe Shklo and the thanatar system, the populations are bisexual (the share of males is 28.5 — 75.0 %), in the lake Bolshoe yarovoe — parthenogenetic (the share of males on average does not exceed 3 %). At the same time, sexual dimorphism is typical for both types of populations: females are larger than males, males have a larger head (the distance between the eyes is greater by 15.5 %, the diameter of the eye is 26.1 %, the length of the antenna is 22.3 %) and a larger number of bristles (36.1 %). The greatest variability is observed in the parameters of the Furka structure associated with the salinity of water by feedback and the pH — line indicator. Significant differences between the samples of males were revealed. The largest number of significant differences in morphometric indicators was found between samples of males from bisexual populations (lake thanatar and lake Maloe Shklo), the smallest — between males from the parthenogenetic population of lake Bolshoe yarovoe and males from lake Maloe Shklo.

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