Article
Environmental Sciences
Thi Lan Anh Dinh, Filipe Aires, Eric Rahn
Summary: This study used a data-driven approach to investigate the sensitivity of Vietnamese robusta coffee yield to weather, identifying two key moments when weather has the largest impact on yield. Depending on the location, these moments can be used to forecast yield anomalies in advance, and the study also found variations in the sensitivity to weather between different regions.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Agronomy
Mathadadoddi Nanjundegowda Thimmegowda, Melekote Hanumanthaiah Manjunatha, Lingaraj Huggi, Huchahanumegowdanapalya Sanjeevaiah Shivaramu, Dadireddihalli Venkatappa Soumya, Lingegowda Nagesha, Hejjaji Sreekanthamurthy Padmashri
Summary: Two multivariate models were compared for yield predictability in Karnataka's rice production based on long-term data. The artificial neural network model (ANN) showed better predictability compared to simple multiple linear regression (SMLR), with smaller observed deviations. However, an underestimation of yield in some districts and overestimation in others was noted, likely due to the model's inability to account for farmers' yield improvement practices under adverse weather conditions. Despite this, the study highlights the applicability of ANN for yield forecasting and agricultural planning.
Article
Biophysics
Vijaya R. Joshi, Maciej J. Kazula, Jeffrey A. Coulter, Seth L. Naeve, Axel Garcia
Summary: Weather conditions play a crucial role in regulating the growth and yield of crops in rain-fed agricultural systems, as shown in this study on maize and soybean in the US central Corn Belt. The use of support vector machine models with weekly rainfall and average air temperature data proved to be the most accurate for yield estimation compared to other models.
INTERNATIONAL JOURNAL OF BIOMETEOROLOGY
(2021)
Article
Environmental Sciences
El houssaine Bouras, Lionel Jarlan, Salah Er-Raki, Riad Balaghi, Abdelhakim Amazirh, Bastien Richard, Said Khabba
Summary: This study develops an early forecasting model of cereal yields using machine learning algorithms and remote sensing drought indices, climate, and weather variables. Combining satellite drought indices with climate and weather data can improve prediction performance, with satellite drought indices being crucial for predicting yields close to harvest, while weather data and climate indices are key for earlier predictions.
Article
Environmental Sciences
Delphine Renard, Lucie Mahaut, Frederik Noack
Summary: Weather extremes like droughts and heat waves are becoming more frequent globally, impacting agricultural production and food security. Increasing crop diversity at the country level can help mitigate the negative effects of these extreme weather events on agricultural outputs.
ENVIRONMENTAL RESEARCH LETTERS
(2023)
Article
Agronomy
Jessica Paranhos, Wheeler Foshee, Timothy Coolong, Brian Heyes, Melba Salazar-Gutierrez, Kathelyn Kesheimer, Andre Luiz Biscaia Ribeiro da Silva
Summary: This study evaluated the performance of ten commercial sweet corn cultivars under various environmental conditions in the southeastern U.S. and described the impacts of weather variability on cultivar development, yield, and ear quality. The daily air temperature in spring and fall had the greatest influence on yield and ear quality, with spring having a longer growing season and higher yield. The cultivars with the most potential against environmental stresses and best performance were Affection, GSS1170, Passion, and SCI336 in spring, and Affection, GSS1170, and SC1136 in fall.
Article
Environmental Sciences
Mailson Freire de Oliveira, Brenda Valeska Ortiz, Guilherme Trimer Morata, Andres-F Jimenez, Glauco de Souza Rolim, Rouverson Pereira da Silva
Summary: Methods using remote sensing and artificial intelligence can predict corn yield at the management zone level by integrating spectral, topographic, and wetness information. The results show that accurate corn yield predictions can be made using spectral crop information during the flowering growth stage. Site-specific models improve the accuracy of yield forecasting in management zones.
Article
Law
A. Bodas-Salcedo, J. M. Gregory, D. M. H. Sexton, C. P. Morice
Summary: We developed a statistical method to assess CMIP6 simulations of surface temperature change, considering variability, uncertainty, and ensemble size. This method is a useful tool due to its generality and incorporation of information about unforced variability.
Article
Green & Sustainable Science & Technology
Van Quang Do, Mai Lan Phung, Duc Toan Truong, Thi Thanh Trang Pham, Van Thanh Dang, The Kien Nguyen
Summary: Vietnam, located in the tropical monsoon region, frequently experiences extreme weather events such as storms and droughts. These events, combined with climate change, are having a negative impact on agriculture and fishery enterprises in the Central and Central Highlands regions of Vietnam. The study highlights the need for appropriate measures to adapt and mitigate the impacts of extreme weather events and climate change on the country's economic sectors.
Article
Biology
Yanxi Zhao, Dengpan Xiao, Huizi Bai, De Li Liu, Jianzhao Tang, Yongqing Qi, Yanjun Shen
Summary: Understanding the impact of climate change on crop production and water consumption is crucial for sustainable agricultural development. This study investigated the effects of temperature, solar radiation, precipitation, and CO2 concentration on crop phenology, yield, and water consumption in a rice-wheat rotation system. The findings revealed that climate change significantly influences the growth process, yield, and water use efficiency of crops.
Article
Environmental Sciences
Ariel Ortiz-Bobea, Toby R. Ault, Carlos M. Carrillo, Robert G. Chambers, David B. Lobel
Summary: Historically, agricultural productivity has grown, but the impact of climate change on this growth is unclear. Over the last 60 years, anthropogenic climate change has decreased global agricultural total factor productivity by 21%, with stronger effects in warmer regions. Global agriculture is becoming more vulnerable to ongoing climate change.
NATURE CLIMATE CHANGE
(2021)
Article
Agronomy
Hyo Jin Lee, Hyun Hwa Park, Young Ok Kim, Yong In Kuk
Summary: Agro-photovoltaics (APV) can lead to sustainable development in agricultural areas, but constant shading from APV structures poses a major challenge. This study investigated the growth and yield optimization of rice, potato, sesame, and soybean crops under different APV systems. The results showed that different crops had varying responses to APV systems, with some crops performing similarly to the control plots and others experiencing yield reductions. Factors such as plant height and stem length were generally higher in crops grown under APV systems, while solar radiation and photosynthetic efficiency were lower.
Article
Agronomy
Kurt Heil, Christian Kloepfer, Kurt-Juergen Huelsbergen, Urs Schmidhalter
Summary: This study aims to identify the weather parameters that could describe the influence on agricultural yields, evaluate the observed weather conditions, and explore the relationship between weather events and winter wheat yields. The findings indicate that heat waves and dry periods significantly affect winter wheat yields, and the past 20 years have seen recurrent low precipitation and high temperature incidents, leading to substantial yield losses.
Article
Environmental Sciences
Stefano Marino, Arturo Alvino
Summary: This study utilized remote sensing techniques for wheat cultivar classification and agronomic trait detection. The cluster method based on vegetation indices showed significant ability in monitoring and evaluating wheat crop agronomic traits.
Article
Environmental Sciences
Zhongjie Yu, Timothy J. Griffis, John M. Baker
Summary: Research indicates that climate change has significant impacts on CO2 uptake in croplands and natural ecosystems in the Corn Belt region, with early crop growth stages experiencing an increase in net CO2 uptake, but peak growing seasons seeing a decrease. Future projections suggest that an increase in summer temperatures may result in a reduction of annual CO2 sequestration in the Corn Belt.
COMMUNICATIONS EARTH & ENVIRONMENT
(2021)
Article
Agronomy
Wenyi Xu, Bo Elberling, Per Lennart Ambus
Summary: The frequency and extent of wildfires in the Arctic have been increasing due to climate change. In this study, researchers conducted experiments in West Greenland to investigate the long-term impacts of climate warming on post-fire carbon dioxide exchange in arctic tundra ecosystems. They found that fire increased soil organic phosphorus concentrations and burned areas remained a net CO2 source five years after the fire. However, with four to five years of summer warming, the burned areas turned into a net CO2 sink.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yuanhang Yang, Jiabo Yin, Shengyu Kang, Louise J. Slater, Xihui Gu, Aliaksandr Volchak
Summary: This study investigates the impacts of water and heat stress on carbon uptake in China and explores the driving mechanisms of droughts using a machine learning model. The results show that droughts are mostly driven by atmospheric dryness, with precipitation, relative humidity, and temperature playing dominant roles. Water and heat stress have negative impacts on carbon assimilation, and drought occurrence is projected to increase significantly in the future. Improving ecosystem resilience to climate warming is crucial in mitigating the negative effects of droughts on carbon uptake.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Mingjun Wang, Lu Zhao, Ziling He, Yu Feng, Yaosheng Wang, Daozhi Gong, Chunwei Liu, Rangjian Qiu
Summary: This study proposes a method to partition evapotranspiration (ET) into its components in agroforestry systems. The method is based on water-carbon coupling theory and flux conservation hypothesis. The results show that the partitioned components agree well with measurements from other sensors. The study also finds that atmospheric evaporation demand and vegetation factors greatly influence the components of ET, and increased tree leaf area limits understory grass transpiration.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xinhao Li, Tianshan Zha, Andrew Black, Xin Jia, Rachhpal S. Jassal, Peng Liu, Yun Tian, Chuan Jin, Ruizhi Yang, Feng Zhang, Haiqun Yu, Jing Xie
Summary: With the rapid increase of urbanization, evapotranspiration (ET) in urban forests has become increasingly important in urban hydrology and climate. However, there is still a large uncertainty regarding the factors that regulate ET in urban areas. This study investigates the temporal variations of ET in an urban forest park in Beijing using the eddy-covariance technique. The results show that daily ET is close to zero during winter but reaches 3-6 mm day-1 in summer. Daily ET increases with vapor pressure deficit (VPD) and soil water content (SWC). Monthly ET increases linearly with normalized difference vegetation index and shows a strong correlation with surface conductance (gs), while exhibiting saturated responses to increasing monthly precipitation (PPT). Annual ET ranges from 326 to 566 mm, and soil water replenishment through PPT from the previous year is responsible for the generally higher monthly ET in spring relative to PPT. Biotic factors and PPT seasonality play essential roles in regulating ET at different scales.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Zhaogang Liu, Zhi Chen, Meng Yang, Tianxiang Hao, Guirui Yu, Xianjin Zhu, Weikang Zhang, Lexin Ma, Xiaojun Dou, Yong Lin, Wenxing Luo, Lang Han, Mingyu Sun, Shiping Chen, Gang Dong, Yanhong Gao, Yanbin Hao, Shicheng Jiang, Yingnian Li, Yuzhe Li, Shaomin Liu, Peili Shi, Junlei Tan, Yakun Tang, Xiaoping Xin, Fawei Zhang, Yangjian Zhang, Liang Zhao, Li Zhou, Zhilin Zhu
Summary: This study investigates the responses of temperate grassland (TG) and alpine grassland (AG) to climate change by studying carbon (C) fluxes across different regions in China. The results reveal that water factors consistently increase C fluxes, while temperature factors have opposite effects on TG and AG. The study enhances our understanding of C sinks and grassland sensitivity to climate change.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Peng Li, Huijie Li, Bingcheng Si, Tao Zhou, Chunhua Zhang, Min Li
Summary: This study mapped the distribution of forest age on the Chinese Loess Plateau using the LandTrendr algorithm. The results show that the LT algorithm is a convenient, efficient, and reliable method for identifying forest age. The findings have important implications for assessing and quantifying biomass and carbon sequestration in afforestation efforts on the Chinese Loess Plateau.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Review
Agronomy
Yean-Uk Kim, Heidi Webber, Samuel G. K. Adiku, Rogerio de S. Noia Junior, Jean-Charles Deswarte, Senthold Asseng, Frank Ewert
Summary: As climate change is expected to increase the intensity and frequency of extreme weather events, it is crucial to assess their impact on cropping systems and explore adaptation options. Process-based crop models (PBCMs) have improved in simulating the impacts of major extreme weather events, but still struggle to reproduce low crop yields under wet conditions. This article provides an overview of the yield-loss mechanisms of excessive rainfall in cereals and the associated modelling approaches, aiming to guide improvements in PBCMs.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Xiaodong Liu, Yingjie Feng, Xinyu Zhao, Zijie Cui, Peiling Liu, Xiuzhi Chen, Qianmei Zhang, Juxiu Liu
Summary: Understanding the impact of climate on litterfall production is crucial for simulating nutrient cycling in forest ecosystems. This study analyzed a 14-year litterfall dataset from two subtropical forests in South China and found that litterfall was mainly influenced by wind speed during the wet season and by temperature during the dry season. These findings have potential significance in improving our understanding of carbon and nutrient cycling in subtropical forest ecosystems under climate change conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Ruonan Chen, Liangyun Liu, Zhunqiao Liu, Xinjie Liu, Jongmin Kim, Hyun Seok Kim, Hojin Lee, Genghong Wu, Chenhui Guo, Lianhong Gu
Summary: Solar-induced chlorophyll fluorescence (SIF) has the potential to estimate gross primary production (GPP), but the quantitative relationship between them is not constant. In this study, a mechanistic model for SIF-based GPP estimation in evergreen needle forests (ENF) was developed, considering the seasonal variation in a key parameter of the model. The GPP estimates from this model were more accurate compared to other benchmark models, especially in extreme conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jingyi Zhu, Yanzheng Yang, Nan Meng, Ruonan Li, Jinfeng Ma, Hua Zheng
Summary: This study developed a random forest model using climate station and satellite data to generate high-precision precipitation datasets for the Qinghai-Tibet Plateau. By incorporating multisource satellite data, the model achieved a significant enhancement in precipitation accuracy and showed promising results in regions with limited meteorological stations and substantial spatial heterogeneity in precipitation patterns.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Yulin Yan, Youngryel Ryu, Bolun Li, Benjamin Dechant, Sheir Afgen Zaheer, Minseok Kang
Summary: Sustainable rice farming practices are urgently needed to meet increasing food demand, cope with water scarcity, and mitigate climate change. Traditional farming methods that prioritize a single objective have proven to be insufficient, while simultaneously optimizing multiple competing objectives remains less explored. This study optimized farm management to increase rice yield, reduce irrigation water consumption, and tackle the dilemma of reducing GHG emissions. The results suggest that the optimized management can maintain or even increase crop yield, while reducing water demand and GHG emissions by more than 50%.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Sasha D. Hafner, Jesper N. Kamp, Johanna Pedersen
Summary: This study compared micrometeorological and wind tunnel measurements using a semi-empirical model to understand wind tunnel measurement error. The results showed differences in emission estimates between the two methods, but the ALFAM2 model was able to reproduce emission dynamics for both methods when considering differences in mass transfer. The study provides a template for integrating and comparing measurements from different methods, suggesting the use of wind tunnel measurements for model evaluation and parameter estimation.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Wenfang Xu, Wenping Yuan, Donghai Wu, Yao Zhang, Ruoque Shen, Xiaosheng Xia, Philippe Ciais, Juxiu Liu
Summary: In the summer of 2022, China experienced record-breaking heatwaves and droughts, which had a significant impact on plant growth. The study also found that heatwaves were more critical than droughts in limiting vegetation growth.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Jiaqi Guo, Xiaohong Liu, Wensen Ge, Liangju Zhao, Wenjie Fan, Xinyu Zhang, Qiangqiang Lu, Xiaoyu Xing, Zihan Zhou
Summary: Vegetation photosynthetic phenology is an important indicator for understanding the impacts of climate change on terrestrial carbon cycle. This study evaluated and compared the abilities of different spectral indices to model photosynthetic phenology, and found that NIRv and PRI are effective proxies for monitoring photosynthetic phenology.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)
Article
Agronomy
Arango Ruda Elizabeth, M. Altaf Arain
Summary: Temperate deciduous forests have significant impacts on regional and global water cycles. This study examined the effects of climate change and extreme weather events on the water use and evapotranspiration of a temperate deciduous forest in eastern North America. The results showed that photosynthetically active radiation and air temperature were the primary drivers of evapotranspiration, while vapor pressure deficit regulated water use efficiency. The study also found a changing trend in water use efficiency over the years, influenced by extreme weather conditions.
AGRICULTURAL AND FOREST METEOROLOGY
(2024)