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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biometeorology and Bioclimatology".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 31646

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Dr. Jaromir Krzyszczak

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Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Interests: climate change; climate change adaptation; crop growth and yield prediction; Earth observation; remote sensing in agriculture; spectral data analysis; multifractality of time series; forecasting
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Special Issue Information

Dear Colleagues,

Agrometeorology is an interdisciplinary field of science that places meteorological measurements in an agricultural context by trying to assess which actions will lead to the improvement of agricultural productivity, either by minimizing risks from adverse weather conditions or taking advantage of beneficial aspects of climate. With projected climate change increasing the vulnerability of agricultural production, agriculture will face many serious challenges in the coming decades. Therefore, responding to the growing demand to understand the nature and complexity of interactions in the soil-plant-atmosphere system on various spatial and temporal scales, considering climate change and variability, the journal Atmosphere (ISSN 2073-4433, IF 2.046) has launched a Special Issue entitled “Climate Change and Agrometeorological Time Series”, which I am guest editing. This Special Issue aims to attract researchers from a wide range of research disciplines, especially those dealing with the physics of the atmosphere, soil physics and chemistry, hydrology, meteorology, climatology, crop and animal physiology and phenology, agronomy and others. We invite contributions related to agrometeorology and agrometeorological time series, especially those connected with applications and climate change. Topics of interest for publication include, but are not limited to the following:

Weather and climate-related impacts on agriculture (i.e., risks, extreme events, etc.)
Climate change impacts and mitigation/adaptation in agriculture
Agro-meteorological modeling
Agro-meteorological monitoring, advising, and forecasting
Agriculture–atmosphere interactions at various scales

The submission deadline is 10 September 2020. Submitted papers should not be under consideration for publication elsewhere.

Dr. Jaromir Krzyszczak
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Agro-meteorological time series
Climate
Climate change
Climate change adaptation
Crop yield modeling
Forecasting
Extreme events

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Published Papers (9 papers)

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Research

19 pages, 4438 KiB

Open AccessArticle

Predictive Capacity of Rainfall Data to Estimate the Water Needs of Fruit Plants in Water Deficit Areas

by Piotr Stachowski, Barbara Jagosz, Stanisław Rolbiecki and Roman Rolbiecki

Atmosphere 2021, 12(5), 550; https://doi.org/10.3390/atmos12050550 - 24 Apr 2021

Cited by 6 | Viewed by 3217

Abstract

This study investigated the usefulness of three methods: (1) Press, (2) Grabarczyk and Rzekanowski, and (3) Treder, in estimating the water needs of apple, pear, cherry and plum trees grown in central Poland, where particular water deficits are observed. The assessments were based on meteorological data for the growing seasons 1989–2020. Orchard irrigation requires a simple and accessible method of estimating plant water requirements. The average water needs assessed for apple ranged from 435 mm (Press) to 729 mm (Grabarczyk and Rzekanowski), for pear between 353–699 mm (Grabarczyk and Rzekanowski), for cherry between 315 mm (Press) and 660 mm (Grabarczyk and Rzekanowski), and plum ranged from 455 mm (Press) to 718 mm (Grabarczyk and Rzekanowski). Regardless of the method used, precipitation in the studied period did not cover the water needs of the fruit trees. Additionally, there was a tendency to increase the water requirements of the plants. In each method, water needs in the second and third decades were higher than in the first. The highest water needs of the fruit trees were calculated using the Treder method, and the lowest using the Press method. In practice, each of the methods can be used to forecast the water needs of fruit plants, but the Treder method seems to be the simplest and most accessible. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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16 pages, 2161 KiB

Open AccessArticle

Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

by Yuan Xu, Jieming Chou, Fan Yang, Mingyang Sun, Weixing Zhao and Jiangnan Li

Atmosphere 2021, 12(2), 172; https://doi.org/10.3390/atmos12020172 - 28 Jan 2021

Cited by 14 | Viewed by 3087

Abstract

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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21 pages, 3794 KiB

Open AccessArticle

Can Technological Development Compensate for the Unfavorable Impacts of Climate Change? Conclusions from 50 Years of Maize (Zea mays L.) Production in Hungary

by László Huzsvai, József Zsembeli, Elza Kovács and Csaba Juhász

Atmosphere 2020, 11(12), 1350; https://doi.org/10.3390/atmos11121350 - 12 Dec 2020

Cited by 11 | Viewed by 3037

Abstract

The goals of our study were to evaluate the historical aspects of maize (Zea mays L.) production in Hungary, and to provide a prognosis for the yield for 2050 based on the trends of temperature, precipitation, and climatic water balance changes. Different climate zones for the period of 1970–2019 were investigated by means of correlation analyses, normality tests, time series analysis, and multiple linear regression analysis. Two well-distinguishable linear trends in the yields were found, the first representing large-scale farming, and the second starting with the change of the socio-economic system in 1989. The annual amount of precipitation showed high variations both spatially and temporally, although no significant change was identified for the last five decades. In the period 1990–2019, not only were higher temperatures characteristic, but the frequency of extreme high temperature values (T_max > 30 °C) also increased. We quantified the heat stress, expressing it in heat stress units (HSU, °C) derived from the heat-sum of the daily maximum air temperature values above 30 °C. By 2050, the average increase in HSUs may reach 35 °C. Increasing HSU causes yield depression; according to our estimations, a 1 °C increase in HSU results in a 23 kg ha⁻¹ yield depression of maize. Taking the unfavorable effect of heat stress and technological development into consideration, the average domestic yield of maize will be 8.2 t ha⁻¹. Our study revealed that without taking technological development into consideration, prediction models may overestimate the adverse effect of climate change on crop production. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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21 pages, 6433 KiB

Open AccessArticle

Subsistence Agriculture Productivity and Climate Extreme Events

by Tásia Moura Cardoso do Vale, Maria Helena Constantino Spyrides, Lara De Melo Barbosa Andrade, Bergson Guedes Bezerra and Pollyanne Evangelista da Silva

Atmosphere 2020, 11(12), 1287; https://doi.org/10.3390/atmos11121287 - 29 Nov 2020

Cited by 9 | Viewed by 3663

Abstract

The occurrence of rainfall extreme events leads to several environmental, social, cultural, and economic consequences, heavily impacting agriculture. The analysis of climate extreme indices at the municipal level is of the uttermost importance to the overall study of climate variability and regional food security. Corn, bean, and cassava are among the most cultivated temporary subsistence crops. Thus, the objective of this study was to analyze the relationship between subsistence agriculture productivity and the behavior of rainfall extreme indices in the Rio Grande do Norte state in the period from 1980 to 2013. We used the dataset provided by Xavier (2016) and the climate extreme indices obtained through the Expert Team on Climate Change Detection and Indices. Crop productivity data were retrieved from the Municipal Agriculture Survey from the Brazilian Institute of Geography and Statistics system. The methodology evaluated the behavior and the relationship between agricultural productivity time series and extreme precipitation indicators. We applied the following statistical techniques: descriptive analysis, time series trend analysis by the Mann-Kendall test, cluster analysis, and analysis of variance to check for equal means between identified groups. Cluster analysis was considered an adequate tool for the comprehension of data spatial distribution, allowing the identification of five homogenous subregions with different precipitation patterns. Rainfall extreme indices allowed the analysis of regional conditions regarding consecutive dry days, annual precipitation in wet days, and heavy rainfall. Trends were identified in these indices and they were significantly correlated with dryland crops productivity, indicating a direct relationship between water availability and regional agroclimatic stress. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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15 pages, 4924 KiB

Open AccessArticle

Trends and Persistence of Dry–Wet Conditions in Northeast Brazil

by Antonio Samuel Alves da Silva, Moacyr Cunha Filho, Rômulo Simões Cezar Menezes, Tatijana Stosic and Borko Stosic

Atmosphere 2020, 11(10), 1134; https://doi.org/10.3390/atmos11101134 - 21 Oct 2020

Cited by 13 | Viewed by 2886

Abstract

We analyze trend and persistence in Standardized Precipitation Index (SPI) time series derived from monthly rainfall data at 133 gauging stations in Pernambuco state, Brazil, using a suite of complementary methods to address the spatially explicit tendencies, and persistence. SPI was calculated for 1-, 3-, 6-, and 12-month time scales from 1950 to 2012. We use Mann–Kendall test and Sen’s slope to determine sign and magnitude of the trend, and detrended fluctuation analysis (DFA) method to quantify long-term correlations. For all time scales significant negative trends are obtained in the Sertão (deep inland) region, while significant positive trends are found in the Agreste (intermediate inland), and Zona da Mata (coastal) regions. The values of DFA exponents show different scaling behavior for different time scales. For short-term conditions described by SPI-1 the DFA exponent is close to 0.5 indicating weak persistency and low predictability, while for medium-term conditions (SPI-3 and SPI-6) DFA exponents are greater than 0.5 and increase with time scale indicating stronger persistency and higher predictability. For SPI-12 that describes long-term precipitation patterns, the values of DFA exponents for inland regions are around 1, indicating strong persistency, while in the shoreline the value of the DFA exponent is between 1.0 and 1.5, indicating anti-persistent fractional Brownian motion. These results should be useful for agricultural planning and water resource management in the region. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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24 pages, 2868 KiB

Open AccessEditor’s ChoiceArticle

Multifractal Cross Correlation Analysis of Agro-Meteorological Datasets (Including Reference Evapotranspiration) of California, United States

by Adarsh Sankaran, Jaromir Krzyszczak, Piotr Baranowski, Archana Devarajan Sindhu, Nandhineekrishna Pradeep Kumar, Nityanjali Lija Jayaprakash, Vandana Thankamani and Mumtaz Ali

Atmosphere 2020, 11(10), 1116; https://doi.org/10.3390/atmos11101116 - 18 Oct 2020

Cited by 13 | Viewed by 3534

Abstract

The multifractal properties of six acknowledged agro-meteorological parameters, such as reference evapotranspiration (ET0), wind speed (U), incoming solar radiation (SR), air temperature (T), air pressure (P), and relative air humidity (RH) of five stations in California, USA were examined. The investigation of multifractality of datasets from stations with differing terrain conditions using the Multifractal Detrended Fluctuation Analysis (MFDFA) showed the existence of a long-term persistence and multifractality irrespective of the location. The scaling exponents of SR and T time series are found to be higher for stations with higher altitudes. Subsequently, this study proposed using the novel multifractal cross correlation (MFCCA) method to examine the multiscale-multifractal correlations properties between ET0 and other investigated variables. The MFCCA could successfully capture the scale dependent association of different variables and the dynamics in the nature of their associations from weekly to inter-annual time scales. The multifractal exponents of P and U are consistently lower than the exponents of ET0, irrespective of station location. This study found that joint scaling exponent was nearly the average of scaling exponents of individual series in different pairs of variables. Additionally, the α-values of joint multifractal spectrum were lower than the α values of both of the individual spectra, validating two universal properties in the MFCCA studies for agro-meteorological time series. The temporal evolution of cross-correlation determined by the MFCCA successfully captured the dynamics in the nature of associations in the P-ET0 link. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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19 pages, 753 KiB

Open AccessArticle

Using a Statistical Crop Model to Predict Maize Yield by the End-Of-Century for the Azuero Region in Panama

by Marlemys M. Martínez, Tosiyuki Nakaegawa, Reinhardt Pinzón, Shoji Kusunoki, Román Gordón and Javier E. Sanchez-Galan

Atmosphere 2020, 11(10), 1097; https://doi.org/10.3390/atmos11101097 - 14 Oct 2020

Cited by 5 | Viewed by 3381

Abstract

In this article, we evaluate the impact of temperature and precipitation at the end of the 21st century (2075–2099) on the yield of maize in the Azuero Region in Panama. Using projected data from an atmospheric climate model, MRI-ACGM 3.2S, the study variables [...] Read more.

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) under four different sea surface Temperature (SST) Ensembles (C0, C1, C2, and C3) and in three different planting dates (21 August, 23 September, and 23 October). In terms climate, results confirm the increase in temperatures and precipitation intensity that has been projected for the region at the end of the century. Moreover, differences are found in the average precipitation patterns of each SST-ensemble, which leads to difference in maize yield. SST-Ensembles C0, C1, and C3 predict a doubling of the yield observed from baseline period (1990–2003), while in C1, the yield is reduced around 5%. Yield doubling is attributed to the increase in rainfall, while yield decrease is related to the selection of a later planting date, which is indistinct to the SST-ensembles used for the calculation. Moreover, lower yields are related to years in which El Niño Southerm Oscilation (ENSO) are projected to occur at the end of century. The results are important as they provide a mitigation strategy for maize producers under rainfed model on the Azuero region, which is responsible for over 95% of the production of the country. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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20 pages, 6484 KiB

Open AccessArticle

Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

by Qiuling Wang, Wei Li, Chan Xiao and Wanxiu Ai

Atmosphere 2020, 11(9), 1011; https://doi.org/10.3390/atmos11091011 - 21 Sep 2020

Cited by 13 | Viewed by 3076

Abstract

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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18 pages, 2930 KiB

Open AccessEditor’s ChoiceArticle

Better Agronomic Management Increases Climate Resilience of Maize to Drought in Tanzania

by Wei Xiong and Elena Tarnavsky

Atmosphere 2020, 11(9), 982; https://doi.org/10.3390/atmos11090982 - 14 Sep 2020

Cited by 7 | Viewed by 4157

Abstract

Improved access to better seeds and other inputs, as well as to market and financing, provides greater harvest security for smallholder farmers in Africa, boosting their incomes and increasing food security. Since 2015, a variety of agronomic measures have been introduced and adopted by smallholder farmers under a program led by the United Nations’ World Food Program (WFP) called the Patient Procurement Platform (PPP). Here, we integrate a variety of agronomic measures proposed by the PPP to more than 20,000 smallholder farmers in Tanzania into 18 management strategies. We apply these across the country through grid-based crop model (DSSAT) simulations in order to quantify their benefits and risk to regional food security and smallholder farmers’ livelihoods. The simulation demonstrates current maize yields are far below potential yields in the country. Simulated yields across the nation were slightly higher than the mean of reported values from 1984 to 2014. Periodic droughts delayed farmers’ sowing and reduced maize yield, leading to high risk and low sustainability of maize production in most of the maize areas of the country. Better agronomic management strategies, particularly the combination of long-maturity, drought tolerance cultivars, with high fertilizer input, can potentially increase national maize production by up to five times, promoting Tanzania as a regional breadbasket. Our study provides detailed spatial and temporal information of the yield responses and their spatial variations, facilitating the adoption of various management options for stakeholders. Full article

(This article belongs to the Special Issue Climate Change and Agrometeorological Time Series)

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