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Agrometeorology, Agricultural Water Management and Impacts of Extreme Events

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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 (30 September 2023) | Viewed by 28088

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Special Issue Editors

Dr. Demetrios E. Tsesmelis

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Guest Editor

1. Laboratory of Technology and Policy of Energy and Environment, School of Applied Arts and Sustainable Design, Hellenic Open University, 26335 Patras, Greece
2. Department of Technology Products and Services, NEURPUBLIC S.A., 18545 Piraeus, Greece
Interests: integrated water resources management; drought management; contingency planning; drought vulnerability; desertification vulnerability; composite index; water and land degradation
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Dr. Nikolaos Skondras

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Guest Editor

Global Water Partnership-Mediterranean (GWP-Med), 10556 Athens, Greece
Interests: integrated water resources management; non-conventional water resources; water treatment; wastewater treatment; composite index; water scarcity; water stress; resilience; vulnerability
Special Issues, Collections and Topics in MDPI journals

Ippokratis Gkotsis

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Guest Editor Assistant

Department of Technology Products and Services, NEUROPUBLIC S.A., 18545 Piraeus, Greece
Interests: water resources management; environmental vulnerability; agricultural water

Special Issue Information

Dear Colleagues,

Water is a crucial resource as its availability can affect all aspects of society, the economy and the environment. Today, more than ever, the effects of water availability-related events are getting more and more visible. The recent drought event that took place in Europe (summer 2022) serves as a significant example and an important lesson, a lesson towards adopting more water-friendly and water-saving practices in our everyday activities. Agriculture is such an activity and, as the greatest water-consuming activity, it requires the attention of the scientists, decision makers, producers and end-users. Every individual group has a role to play towards adapting the agricultural practices while setting the milestones for a water- and food-secure future. One agriculture-related factor that threatens water and food security is the loss of water that is taking place in the distribution systems of agricultural water. A second factor is the use of more water that is actually required by the plants.

Such factors can be addressed and the impacts of agriculture on the environment as well as the impact of a water-related events on agriculture and the economy can be minimized through the use of an arsenal of readily available options. The science of agrometeorology, the instruments and methods for the measurement of the actual agriculture water demand, satellite technology, water leakage detectors, precision irrigation (smart farming), decision-making systems, as well as other tools, methods and practices are already here to assist towards this adaptation.

In this regard, this Special Issue focuses on agrometeorology, the management of agricultural water and the impacts of extreme climatic events on the agricultural production. It also encompasses the adaptation of agricultural practices towards climate change resilience and a water- and food-secure future.

Topics of interest include but are not limited to:

Evapotranspiration and other mass (carbon, water, etc.) and energy fluxes;
Evapotranspiration models and evaluation;
Droughts and impacts on plants;
Water stress;
Agricultural water management;
Irrigation management;
Precision irrigation (smart farming);
Water losses;
Water recycling and reuse in irrigation;
Water and food security;
Hydrological processes;
Weather factors' effect on phytopathology and plant diseases;
Effects of temperature and water availability on plants;
Impacts of climate and climate change on agricultural crops;
Agroclimatology;
Remote sensing and crop modeling;
Future projections in agricultural productivity;
Aridity and changes of climate;
Impacts of vegetation on rural microclimate.

Dr. Demetrios E. Tsesmelis
Dr. Nikolaos Skondras
Guest Editors

Ippokratis Gkotsis
Guest Editor Assistant

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Keywords

agricultural water
agricultural production
climate change
resilience
agricultural practices
water losses
forest ecosystems
droughts
drought impacts
water security
food security
irrigation management
decision making
plant growth
plant-weather relations

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Published Papers (10 papers)

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Research

17 pages, 4823 KiB

Open AccessArticle

Agricultural Water Deficit Trends in Yemen

by Hussein Gadain and Brigadier Libanda

Atmosphere 2023, 14(8), 1263; https://doi.org/10.3390/atmos14081263 - 9 Aug 2023

Cited by 3 | Viewed by 2406

Abstract

Globally, climate change is triggering shifts in water availability, especially across arid and desert landscapes similar to that in Yemen, where precipitation patterns are increasingly erratic. Here, we use water budget calculations, drought metrics, and trend analyses to examine climatic water deficits, with the aim of unraveling irrigation demands and overall water stress across Yemen. The results indicate that 94% of the influx is lost back to the atmosphere via evapotranspiration, 6% is converted to runoff, and only a negligible amount, generally less than 1%, is retained as storage. The results also show an unrelenting, statistically significant water deficit increase of 0.17 mm yr⁻¹ on the Sen’s slope, at the critical Z-value of 0.005 across the country, for the past 63 years. Our findings challenge the conventional understanding of water deficits across Yemen and suggest that the country’s water resources situation is direr than was earlier documented. Further results show that while the water shortage mosaic across the country experiences interannual variations, their occurrence is significantly intensifying. As such, an immediate and radical modernization of integrated water management systems, including concerted investments in irrigation and artificial recharge wells, especially across the Arabian Sea Coast, the Red Sea Coast, and the Highlands, is strongly recommended. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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15 pages, 6598 KiB

Open AccessEditor’s ChoiceArticle

Three-Dimensional Visualization of Long-Range Atmospheric Transport of Crop Pathogens and Insect Pests

by Marcel Meyer, William Thurston, Jacob W. Smith, Alan Schumacher, Sarah C. Millington, David P. Hodson, Keith Cressman and Christopher A. Gilligan

Atmosphere 2023, 14(6), 910; https://doi.org/10.3390/atmos14060910 - 23 May 2023

Cited by 3 | Viewed by 3128

Abstract

Some of the most devastating crop diseases and insect pests can be transmitted by wind over extremely long distances. These low-probability but high-impact events can have severe consequences for crop production and food security by causing epidemic outbreaks or devastating insect infestations in previously uninfected geographic areas. Two prominent examples that have recently caused substantial damage to agricultural production are novel strains of wheat rusts and desert locust swarm infestations. Whilst quantitative estimates of long-range atmospheric transport events can be obtained using meteorological transport simulations, the exact characteristics of three-dimensional spatiotemporal dynamics of crop pathogen transport and insect flight on extremely large spatial scales, over entire regions and continents, remain largely unknown. Here, we investigate the feasibility and usefulness of new advanced geospatial data visualization methods for studying extremely long-distance airborne transmission of crop pathogens and insect pests. We combine field surveillance data and a Lagrangian Particle Dispersion Model with novel techniques from computer graphics to obtain, for the first time, detailed three-dimensional visual insights into airborne crop pathogen and insect pest transport on regional and continental scales. Visual insights into long-distance dispersal of pests and pathogens are presented as a series of short 3D movies. We use interactive three-dimensional visual data analysis for explorative examination of long-range atmospheric transport events from a selection of outbreak and infestation sites in East Africa and South East Asia. The practical usefulness of advanced 3D visualization methods for improving risk estimates and early warning is discussed in the context of two operational crop disease and insect pest management systems (for wheat rusts and desert locusts). The tools and methods introduced here can be applied to other pathogens, pests, and geographical areas and can improve understanding of risks posed to agricultural production by crop disease and insect pest transmission caused by meteorological extreme events. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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18 pages, 1327 KiB

Open AccessEditor’s ChoiceArticle

Nexus between Social Vulnerability and Resilience to Agricultural Drought amongst South African Smallholder Livestock Households

by Yonas T. Bahta and Willem A. Lombard

Atmosphere 2023, 14(5), 900; https://doi.org/10.3390/atmos14050900 - 21 May 2023

Cited by 8 | Viewed by 2317

Abstract

Livestock farmers in Sub-Saharan Africa rely on rain-fed agriculture, which exposes them to the risks of agricultural drought. Agricultural drought has become a significant threat to the extreme mortality of livestock, thus negatively impacting social vulnerability and household resilience to agricultural drought and extreme events. Researchers rarely empirically assess the connection between vulnerability and resilience, which are highly related concepts. By measuring and connecting vulnerability and resilience concepts closely related to disasters such as agricultural drought, this article makes a contribution to the body of disaster literature. The study aimed to empirically examine the relationship between smallholder livestock farming households’ social vulnerability and their resilience to agricultural drought. A survey of 217 smallholder livestock farmers was conducted. The Social Vulnerability Index (SVI), the Agricultural Drought Resilience Index (ADRI), and Pearson’s correlation coefficient were used for data analysis. A correlation was identified between resilience to agricultural drought and social vulnerability, indicating that smallholder livestock farmers are more susceptible to harm and lack the means to rebound effectively. Unsurprisingly, the majority of resource-poor smallholder livestock farmers (79%) lack safety nets during agricultural droughts. They are less resilient and more vulnerable households, leading them to social vulnerability. This study provides input/guidance to identify farming households with high social vulnerability and less resilience to threats and their capabilities of recouping and adopting after experiencing an agricultural drought. Additionally, looking at household resilience and social vulnerability to agricultural droughts could provide a way to pinpoint at-risk areas, assisting emergency planners in directing resources and intervention programs to those areas where assistance is most likely to be needed during disasters such as agricultural droughts. This implies that thorough policy intervention programs need to be tailored toward reducing damage or finding the path to recovery. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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16 pages, 3428 KiB

Open AccessEditor’s ChoiceArticle

Climate Indices and Their Impact on Maize Yield in Veracruz, Mexico

by Citlali Villa-Falfán, Ofelia Andrea Valdés-Rodríguez, Jorge Luis Vázquez-Aguirre and Fernando Salas-Martínez

Atmosphere 2023, 14(5), 778; https://doi.org/10.3390/atmos14050778 - 25 Apr 2023

Cited by 5 | Viewed by 2660

Abstract

The State of Veracruz (Mexico) is highly vulnerable to climate change. Therefore, it is necessary to identify and analyze local climate extreme trends and explore potential relationships between climate indices and maize. The objectives of this research were (1) to describe recent trends of climate indices (1979–2018) and (2) to compare these climate indices with maize yields produced in Veracruz, Mexico, under rainfed conditions. The methodology calculated and analyzed the sector-specific climate indices (Rx5day, PRCPTOT, SPI6, R20mm, TXx, TNn, TXgt50p, and TXge35) in 18 observation sites using Climpact. Climate indices were calculated over the spring-summer agricultural cycle and correlated with rainfed maize yields. Results show increasing trends for Rx5day, TXx, TXgt50p, and TXge35 indices in 65%, 56%, 89%, and 67% of the analyzed sites, respectively, whereas decreasing trends in PRCPTOT and R20mm indices were detected in 59% and 47% of the sites. Significant correlations (p < 0.05) between climate indices and maize yield were found in eight municipalities, of which 62% were positive. In conclusion, extreme temperature and precipitation local events are increasing in frequency, duration, and intensity, and depending on the site’s local climate, these might positively or negatively impact maize yields. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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16 pages, 2096 KiB

Open AccessArticle

Soil Water Content at Planting Affects Determining Agricultural Drought for Rainfed Spring Wheat

by Kai Zhang, Funian Zhao and Bo Zhang

Atmosphere 2023, 14(4), 665; https://doi.org/10.3390/atmos14040665 - 31 Mar 2023

Cited by 1 | Viewed by 1643

Abstract

Agricultural drought often refers to water deficit in soil caused by a shortage of precipitation during a specific period in crop growing season, thus leading to final crop production failure. However, soil water content during planting may be related to the final yield of crop. Therefore, the soil water content at planting could have a great impact on determining whether agricultural drought occurs during crop growth and development. In the current study, we used soil water content at planting at 0–50 cm depth, the spring wheat (Triticum aestivum L.) yield from 1987 to 2011, and precipitation from 1971 to 2011 at Dingxi, Gansu Province, China, intending to quantify the influence of soil water content at planting on determining agricultural drought for rainfed spring wheat. The response of spring wheat yield to growing season precipitation comprised two different linear relationships defined by whether the soil water content at planting was greater or less than 100 mm. With the identical amount of soil water content at planting and precipitation during main growth period, a higher soil water content at planting resulted in a greater spring wheat yield. Soil water content at planting was more effective than growing season precipitation for increasing spring wheat yield. According to the probability distributions of soil water content during planting and growing season precipitation, we concluded that 28.6% of the years studued involved agricultural drought for rainfed spring wheat in the Dingxi of Gansu province, China. This analysis, which quantified the relative importance of soil water content at planting (29.97 kg ha⁻¹ per mm) and during growing season precipitation (12.21 kg ha⁻¹ per mm) to determine yield and agricultural drought occurrence for spring wheat, suggests that improving the amount of soil water storage before crop planting is an important way to deal with agricultural drought. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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23 pages, 9668 KiB

Open AccessEditor’s ChoiceArticle

Hydrometeorological Hazards on Crop Production in the State of Veracruz, Mexico

by Ofelia Andrea Valdés-Rodríguez, Fernando Salas-Martínez and Olivia Margarita Palacios-Wassenaar

Atmosphere 2023, 14(2), 287; https://doi.org/10.3390/atmos14020287 - 31 Jan 2023

Cited by 6 | Viewed by 3146

Abstract

Hydrometeorological hazards are considered the most important phenomena affecting crop production in the Eastern regions of Mexico, where the State of Veracruz is located. However, more information about their consequences on these sites needs to be studied. This research aims to determine the effects of hydrometeorological phenomena on the most important crops cultivated in the State of Veracruz. The methodology involved analyzing the State’s crop production database from 2001 to 2020 and comparing this data with the National Hydrometeorological Disaster Declarations database. Multivariable correlation analysis and geographic information systems were applied to geographically analyze 42 rainfed crops plus the five most valuable ones in the State to determine their production related to climatic phenomena. The results found that the most affected crops are corn, soy, sorghum, beans, and rice, with more than 10,000 lost hectares. Droughts caused total damage to corn, soy, and beans and decreased productivity in corn, orange, lemon, wheat, coffee, and sesame. For the most valuable crops, tropical cyclones caused the highest production decrements in corn, sugar cane, and pineapple, while droughts caused the same effects in lemon and orange. We conclude that tropical cyclones are the most critical phenomena negatively impacting Veracruz, with high implications on the agrifood system. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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20 pages, 4163 KiB

Open AccessArticle

Effects of Meteorological Factors on Apple Yield Based on Multilinear Regression Analysis: A Case Study of Yantai Area, China

by Xirui Han, Longbo Chang, Nan Wang, Weifu Kong and Chengguo Wang

Atmosphere 2023, 14(1), 183; https://doi.org/10.3390/atmos14010183 - 15 Jan 2023

Cited by 10 | Viewed by 3010

Abstract

Evaluating the impact of different meteorological conditions on apple yield and predicting the future yield in Yantai City is essential for production. Furthermore, it provides a scientific basis for the increase in apple yield. In this study, first, a grey relational analysis (GRA) was used to determine the quantitative relationship between different meteorological factors and meteorological yield which is defined as affected only by meteorological conditions. Then, the comprehensive meteorological factors extracted by a principal component analysis (PCA) were used as inputs for multiple linear regression (MLR). The apple yield accuracy was compared with the lasso regression prediction. Trend analysis showed that the actual apple yield increased annually, but the meteorological yield decreased annually over a long time. Correlation ranking illustrated that the meteorological yield was significantly correlated with the frost-free period, the annual mean temperature, the accumulated temperature above 10 °C, etc. The good consistency between GRA and MLR–PCA showed that the accumulated temperature above 10 °C, the March–October mean temperature, and the June–August mean temperature are key meteorological factors. In addition, it was found that the principal components

F_{2}, F_{4}

, and

F_{5}

were negatively correlated with meteorological yield, while the principal components

F_{1}

and

F_{3}

were positively correlated with meteorological yield. Moreover, the MLR–PCA model predicted the apple yield in 2020 as 47.256 t·ha⁻¹ with a 7.089% relative error. This work demonstrates that the principal component regression model can effectively extract information about different meteorological factors and improve the model’s accuracy for analyzing key meteorological factors and predicting apple yield. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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21 pages, 10494 KiB

Open AccessArticle

Estimation and Assessment of the Root Zone Soil Moisture from Near-Surface Measurements over Huai River Basin

by En Liu, Yonghua Zhu, Haishen Lü, Robert Horton, Qiqi Gou, Xiaoyi Wang, Zhenzhou Ding, Haiting Xu and Ying Pan

Atmosphere 2023, 14(1), 124; https://doi.org/10.3390/atmos14010124 - 6 Jan 2023

Cited by 8 | Viewed by 2409

Abstract

Root zone soil moisture (RZSM) is a vital variable for agricultural production, water resource management and runoff prediction. Satellites provide large-scale and long-term near-surface soil moisture retrievals, which can be used to estimate RZSM through various methods. In this study, we tested the utility of an exponential filter (ExpF) using in situ soil moisture by optimizing the optimal characteristic time length T_opt for different soil depths. Furthermore, the parameter analysis showed that T_opt correlated negatively with precipitation and had no significant correlation with selected soil properties. Two approaches were taken to obtain T_opt: (1) optimization of the Nash–Sutcliffe efficiency coefficient (NSE); (2) calculation based on annual average precipitation. The precipitation-based T_pre outperformed the station-specific T_opt and stations-averaged T_opt. To apply the ExpF on grid scale, the precipitation-based T_pre considering spatial variability was adopted in the ExpF to obtain RZSM from a new soil moisture dataset RF_SMAP_L3_P (Random Forest Soil Moisture Active Passive_L3_Passive) continuous in time and space over Huai River Basin. Finally, the performance of RF_SMAP_L3_P RZSM (0–100 cm) was evaluated using in situ measurements and compared with mainstream products, for instance, Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity Level 4 (SMOS L4) RZSM. The results indicated that RF_SMAP_L3_P RZSM could captured the temporal variation of measured RZSM best with R value of 0.586, followed by SMAP L4, which had the lowest bias value of 0.03, and SMOS L4 significantly underestimated the measured RZSM with bias value of −0.048 in the basin. Higher accuracy of RF_SMAP_L3_P RZSM was found in the flood period compared with the non-flood period, which indicates a better application for ExpF in wetter weather conditions. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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14 pages, 3214 KiB

Open AccessArticle

Study on the Piecewise Inverse Model of Accumulated Temperature Based on Skewness-Distribution Parameters of Canopy Images in Pepper

by Pei Zhang, Zhengyi Yao, Rong Wang, Jibo Zhang, Mingqian Zhang, Yifang Ren, Xiaoping Xie, Fuzheng Wang, Hongyan Wu and Haidong Jiang

Atmosphere 2023, 14(1), 7; https://doi.org/10.3390/atmos14010007 - 20 Dec 2022

Cited by 1 | Viewed by 1505

Abstract

The crop leaf color is tightly connected with its meteorological environment. Color gradation skewness-distribution (CGSD) parameters can describe the information of leaf color more accurately, systematically, and comprehensively from five dimensions. We took photographs of pepper growing in the greenhouse at a fixed time every day and observed the meteorological factors. The results showed that the CGSD parameters were significantly correlated with meteorological factors, especially with the accumulated temperature, which showed the strongest correlation. Since the relationship between canopy leaf color and accumulated temperature is nonlinear, the piecewise inversion models were constructed by taking the stationary point of the high-order response model of G_skewness to accumulated temperature as the point of demarcation. The rate of outliers had decreased by 57.72%; moreover, the overall inversion accuracy had increased by 3.31% compared with the linear model directly constructed by the stepwise regression. It was observed that the pepper in the greenhouse had a different response to the same meteorological environmental stimulus before and after the stationary point. This study will provide a new method for constructing crop growth models in future research. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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19 pages, 3637 KiB

Open AccessArticle

Actual Evapotranspiration Estimation Using Sentinel-1 SAR and Sentinel-3 SLSTR Data Combined with a Gradient Boosting Machine Model in Busia County, Western Kenya

by Peter K. Musyimi, Ghada Sahbeni, Gábor Timár, Tamás Weidinger and Balázs Székely

Atmosphere 2022, 13(11), 1927; https://doi.org/10.3390/atmos13111927 - 18 Nov 2022

Cited by 6 | Viewed by 3623

Abstract

Kenya is dominated by a rainfed agricultural economy. Recurrent droughts influence food security. Remotely sensed data can provide high-resolution results when coupled with a suitable machine learning algorithm. Sentinel-1 SAR and Sentinel-3 SLSTR sensors can provide the fundamental characteristics for actual evapotranspiration (AET) estimation. This study aimed to estimate the actual monthly evapotranspiration in Busia County in Western Kenya using Sentinel-1 SAR and Sentinel-3 SLSTR data with the application of the gradient boosting machine (GBM) model. The descriptive analysis provided by the model showed that the estimated mean, minimum, and maximum AET values were 116, 70, and 151 mm/month, respectively. The model performance was assessed using the correlation coefficient (r) and root mean square error (RMSE). The results revealed a correlation coefficient of 0.81 and an RMSE of 10.7 mm for the training dataset (80%), and a correlation coefficient of 0.47 and an RMSE of 14.1 mm for the testing data (20%). The results are of great importance scientifically, as they are a conduit for exploring alternative methodologies in areas with scarce meteorological data. The study proves the efficiency of high-resolution data retrieved from Sentinel sensors coupled with machine learning algorithms, focusing on GBM as an alternative to accurately estimate AET. However, the optimal solution would be to obtain direct evapotranspiration measurements. Full article

(This article belongs to the Special Issue Agrometeorology, Agricultural Water Management and Impacts of Extreme Events)

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