scholarly journals Climate Sensitivity and the Value of Agricultural Production in the Brazilian Northeast: An Approach Using Spatial Panel Data

2021 ◽  
Vol 12 (5) ◽  
pp. 65
Author(s):  
Helson Gomes De Souza ◽  
Pablo Urano de Carvalho Castelar ◽  
Edward Martins Costa ◽  
Francisco Jose Silva Tabosa
Keyword(s):  
Agricultural Production ◽  
Spatial Econometric ◽  
Average Temperature ◽  
Production Value ◽  
Spatial Panel Data ◽  
Econometric Methodology ◽  
Temperature Levels ◽  
Space Data ◽  
Brazilian Northeast ◽  
Precipitation And Temperature

This work analyzes the sensitivity of agricultural production in relation to changes in precipitation and temperature levels in the Northeast region of Brazil. For that purpose, data from 2006 to 2016 were used for 952 municipalities in the area. The econometric methodology derived from Kunwar and Bohara (2017) and is applied to the production value of municipalities in the Brazilian Northeast, where it is assumed that the production value is also impacted by the interactions between precipitation and temperature. Thus, time and space data are used, applied to a spatial econometric methodology. The results suggest that the agricultural production of the municipalities of the Brazilian Northeast is spatially autocorrelated. There is indication that there are municipalities with high (or low) levels of production, which have neighbors with these same characteristics. It was also verified that, from 2006 to 2011, the agricultural production was more sensitive to changes in temperature levels than to changes in average precipitation. However, after the years 2012, 2013, 2015 and 2016, agricultural production has become more sensitive to changes in the precipitation levels. It was also noted that in the analyzed period there was an increase in the average sensitivity of the agricultural production in relation to the precipitation levels, while the average temperature sensitivity showed a decrease.

Effect of Climate Change on Agricultural Production

2022 ◽  
pp. 99-114
Author(s):  
Helena Esteves Correia ◽  
Daniela de Vasconcelos Teixeira Agu Costa
Keyword(s):  
Climate Change ◽  
Environmental Factors ◽  
Light Intensity ◽  
Soil Water ◽  
Agricultural Production ◽  
Co2 Concentration ◽  
Atmospheric Co2 ◽  
Air Humidity ◽  
Ozone Level ◽  
Average Temperature

Agricultural production is influenced by environmental factors such as temperature, air humidity, soil water, light intensity, and CO2 concentration. However, climate change has influenced the values of average temperature, precipitation, global atmospheric CO2 concentration, or ozone level. Thus, climate change could lead to different situations on plants and consequently influence agricultural production. With this chapter, the authors intend to research how climate change influences some plant metabolisms (such as photosynthesis, photorespiration, transpiration, among others) and therefore agricultural production.

scholarly journals THE DYNAMIC OF WATER RESOURCES IN NORTH-EASTERN MOROCCO BETWEEN REALITY AND EFFECTS OF CLIMATE CHANGE : CASE OF THE TARIFA PLAIN

2021 ◽  
Vol 03 (06) ◽  
pp. 38-47
Author(s):  
Mustapha BENASQUAR ◽  
Ghazi ABDELKHALEK
Keyword(s):  
Climate Change ◽  
Water Resources ◽  
Agricultural Production ◽  
Agricultural Sector ◽  
Climatic Changes ◽  
Global System ◽  
Natural Factors ◽  
North Eastern ◽  
Eastern Morocco ◽  
Precipitation And Temperature

The issue of climate change today has become one of the issues that receive increasing attention on the part of the global system, due to its disastrous effects at all levels, and this is due to human and natural factors, including the southern bank of the Mediterranean which has not been excluded from, especially the Tarifa Plain in the far northeast of Morocco, which is one of the most important irrigated areas in the country due to its great contribution to agricultural production and its reliance on achieving economic and social development in the region. However, its climate during the last six decades has witnessed clear variations in the rates of precipitation and temperature, whether annual or monthly, or even seasonal and daily. This increases the severity of the climatic drought, which in turn affects water resources. Therefore, it is imperative that great efforts must be made to limit the effects of these changes in light of the excessive depletion of water in the agricultural sector. Through this intervention, we aim to highlight the climatic changes that occurred on the Tarifa Plain, and their repercussions on its water resources, and how to adapt these changes to achieving sustainable development for the studied area.

scholarly journals Estimating Changes in Global Temperature since the Preindustrial Period

2017 ◽  
Vol 98 (9) ◽  
pp. 1841-1856 ◽  
Author(s):  
Ed Hawkins ◽  
Pablo Ortega ◽  
Emma Suckling ◽  
Andrew Schurer ◽  
Gabi Hegerl ◽  
...  
Keyword(s):  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Temperature ◽  
Radiative Forcings ◽  
Global Average ◽  
Average Temperature ◽  
Climate Model Simulations ◽  
Temperature Levels ◽  
Definition Of

Abstract The United Nations Framework Convention on Climate Change (UNFCCC) process agreed in Paris to limit global surface temperature rise to “well below 2°C above pre-industrial levels.” But what period is preindustrial? Somewhat remarkably, this is not defined within the UNFCCC’s many agreements and protocols. Nor is it defined in the IPCC’s Fifth Assessment Report (AR5) in the evaluation of when particular temperature levels might be reached because no robust definition of the period exists. Here we discuss the important factors to consider when defining a preindustrial period, based on estimates of historical radiative forcings and the availability of climate observations. There is no perfect period, but we suggest that 1720–1800 is the most suitable choice when discussing global temperature limits. We then estimate the change in global average temperature since preindustrial using a range of approaches based on observations, radiative forcings, global climate model simulations, and proxy evidence. Our assessment is that this preindustrial period was likely 0.55°–0.80°C cooler than 1986–2005 and that 2015 was likely the first year in which global average temperature was more than 1°C above preindustrial levels. We provide some recommendations for how this assessment might be improved in the future and suggest that reframing temperature limits with a modern baseline would be inherently less uncertain and more policy relevant.

Spatial and temporal characterization of climate at regional scale using homogeneous monthly precipitation and air temperature data: an application in Calabria (southern Italy)

2014 ◽  
Vol 46 (4) ◽  
pp. 629-646 ◽  
Author(s):  
T. Caloiero ◽  
G. Buttafuoco ◽  
R. Coscarelli ◽  
E. Ferrari
Keyword(s):  
Regional Scale ◽  
Kendall Test ◽  
Temperature Data ◽  
Winter Period ◽  
Positive Trend ◽  
Monthly Precipitation ◽  
Average Temperature ◽  
Increasing Trend ◽  
Precipitation And Temperature

In the present study, an approach for a climate characterization based on a statistical analysis of monthly precipitation and temperature data is presented. First, the original database (1916–2010) was homogenized and a geostatistical analysis was carried out to characterize the monthly mean distribution of the two variables in the study area. Then, temporal change of precipitation and temperature were evaluated through the Mann–Kendall test. Finally, to better assess the climate patterns in Calabria, a climatic characterization was carried out by means of the Péguy climograph. Results have shown a decreasing trend for autumn–winter rainfall and an increasing trend in summer. With respect to the average temperature, the analyses revealed a positive trend in late spring and summer, mainly due to the increase in the minimum values, and a negative trend in the autumn–winter period, mainly due to a decrease in the maximum values. The analysis of the Péguy climographs allowed the dataset to be divided into three groups, depending on the different elevation of the gauges. Moreover, different temporal behaviours were detected by analysing the climographs in three sub-periods.

Spatial Econometric Analysis of Urban and County-level Economic Growth Convergence in China

2016 ◽  
Vol 41 (4) ◽  
pp. 410-447 ◽  
Author(s):  
Guangdong Li ◽  
Chuanglin Fang
Keyword(s):  
Economic Growth ◽  
Human Capital ◽  
Panel Data ◽  
Population Growth ◽  
Convergence Rates ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Growth Convergence ◽  
Savings Rate ◽  
Spatial Panel Data

Economic growth convergence, one of the classical assumption in regional economic growth, has been perplexing. There are many empirical studies trying to test if there is regional convergence in China. In this article, we bring new information of the finer spatial scale to the existing literature by using neoclassical convergence analysis, cross-sectional specifications, panel data models, and spatial econometric techniques to test the convergence hypothesis across 2,286 cities and counties in China. Empirical findings from cross-sectional data and spatial panel data show that significant absolute β and conditional β convergence are present in gross domestic product per capita after controlling for investment return rate, human capital, savings rate, population growth, technology advancement, capital depreciation rate, and initial technology level. We also find spatial agglomeration in urban and county economic growth is strong, and spatial effects are significant. Urban and county economic growth convergence rates for 1992–2010 show a gradually accelerated development trend. We present significant evidence that levels of investment, human capital, and initial technology impose significant facilitating effects on city and county economic growth, while savings and population growth have significant negative effects. And city and county economic growth differ in terms of convergence levels and influential factors.

scholarly journals Multidimensional extremes: joint study of precipitation and temperature time series

2021 ◽  
Author(s):  
Beatrix Izsák ◽  
Tamás Szentimrey ◽  
Mónika Lakatos ◽  
Rita Pongrácz
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Values ◽  
Standardized Precipitation Index ◽  
Single Element ◽  
Average Temperature ◽  
Temperature And Precipitation ◽  
The Standardized Precipitation Index ◽  
Climatic Time Series ◽  
Precipitation And Temperature

<p>To study climate change, it is essential to analyze extremes as well. The study of extremes can be done on the one hand by examining the time series of extreme climatic events and on the other hand by examining the extremes of climatic time series. In the latter case, if we analyze a single element, the extreme is the maximum or minimum of the given time series. In the present study, we determine the extreme values of climatic time series by examining several meteorological elements together and thus determining the extremes. In general, the main difficulties are connected with the different probability distribution of the variables and the handling of the stochastic connection between them. The first issue can be solved by the standardization procedures, i.e. to transform the variables into standard normal ones. For example, the Standardized Precipitation Index (SPI) uses precipitation sums assuming gamma distribution, or the standardization of temperature series assumes normal distribution. In case of more variables, the problem of stochastic connection can be solved on the basis of the vector norm of the variables defined by their covariance matrix. According to this methodology we have developed a new index in order to examine the precipitation and temperature variables jointly. We present the new index with the mathematical background, furthermore some examples for spatio-temporal examination of these indices using our software MASH (Multiple Analysis of Series for Homogenization; Szentimrey) and MISH (Meteorological Interpolation based on Surface Homogenized Data Basis; Szentimrey, Bihari). For our study, we used the daily average temperature and precipitation time series in Hungary for the period 1870-2020. First of all, our analyses indicate that even though some years may not be considered extreme if only either precipitation or average temperature is taken in to account, but examining the two elements together these years were extreme years indeed. Based on these, therefore, the study of the extremes of multidimensional climate time series complements and thus makes the study of climate change more efficient compared to examining only one-dimensional time series.</p>

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