scholarly journals The Implication of Different Sets of Climate Variables on Regional Maize Yield Simulations

Atmosphere ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 180
Author(s):  
Amit Kumar Srivastava ◽  
Andrej Ceglar ◽  
Wenzhi Zeng ◽  
Thomas Gaiser ◽  
Cho Miltin Mboh ◽  
...  
Keyword(s):  
Crop Yield ◽  
Large Scale ◽  
Regional Scale ◽  
Maize Yield ◽  
Weather Data ◽  
Climate Data ◽  
Agricultural Planning ◽  
Input Variables ◽  
Different Sources ◽  
Grid Based

High-resolution and consistent grid-based climate data are important for model-based agricultural planning and farm risk assessment. However, the application of models at the regional scale is constrained by the lack of required high-quality weather data, which may be retrieved from different sources. This can potentially introduce large uncertainties into the crop simulation results. Therefore, in this study, we examined the impacts of grid-based time series of weather variables assembled from the same data source (Approach 1, consistent dataset) and from different sources (Approach 2, combined dataset) on regional scale crop yield simulations in Ghana, Ethiopia and Nigeria. There was less variability in the simulated yield under Approach 1, ranging to 58.2%, 45.6% and 8.2% in Ethiopia, Nigeria and Ghana, respectively, compared to those simulated using datasets retrieved under Approach 2. The two sources of climate data evaluated here were capable of producing both good and poor estimates of average maize yields ranging from lowest RMSE = 0.31 Mg/ha in Nigeria to highest RMSE = 0.78 Mg/ha under Approach 1 in Ghana, whereas, under Approach 2, the RMSE ranged from the lowest value of 0.51 Mg/ha in Nigeria to the highest of 0.72 Mg/ha in Ethiopia under Approach 2. The obtained results suggest that Approach 1 introduces less uncertainty to the yield estimates in large-scale regional simulations, and physical consistency between meteorological input variables is a relevant factor to consider for crop yield simulations under rain-fed conditions.

scholarly journals Combining Optical, Fluorescence, Thermal Satellite, and Environmental Data to Predict County-Level Maize Yield in China Using Machine Learning Approaches

2019 ◽  
Vol 12 (1) ◽  
pp. 21 ◽  
Author(s):  
Liangliang Zhang ◽  
Zhao Zhang ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
Fulu Tao
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
County Level ◽  
Climate Data ◽  
Source Data ◽  
Optical Fluorescence

Maize is an extremely important grain crop, and the demand has increased sharply throughout the world. China contributes nearly one-fifth of the total production alone with its decreasing arable land. Timely and accurate prediction of maize yield in China is critical for ensuring global food security. Previous studies primarily used either visible or near-infrared (NIR) based vegetation indices (VIs), or climate data, or both to predict crop yield. However, other satellite data from different spectral bands have been underutilized, which contain unique information on crop growth and yield. In addition, although a joint application of multi-source data significantly improves crop yield prediction, the combinations of input variables that could achieve the best results have not been well investigated. Here we integrated optical, fluorescence, thermal satellite, and environmental data to predict county-level maize yield across four agro-ecological zones (AEZs) in China using a regression-based method (LASSO), two machine learning (ML) methods (RF and XGBoost), and deep learning (DL) network (LSTM). The results showed that combining multi-source data explained more than 75% of yield variation. Satellite data at the silking stage contributed more information than other variables, and solar-induced chlorophyll fluorescence (SIF) had an almost equivalent performance with the enhanced vegetation index (EVI) largely due to the low signal to noise ratio and coarse spatial resolution. The extremely high temperature and vapor pressure deficit during the reproductive period were the most important climate variables affecting maize production in China. Soil properties and management factors contained extra information on crop growth conditions that cannot be fully captured by satellite and climate data. We found that ML and DL approaches definitely outperformed regression-based methods, and ML had more computational efficiency and easier generalizations relative to DL. Our study is an important effort to combine multi-source remote sensed and environmental data for large-scale yield prediction. The proposed methodology provides a paradigm for other crop yield predictions and in other regions.

scholarly journals Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods

2021 ◽  
Vol 13 (18) ◽  
pp. 3760
Author(s):  
Linghua Meng ◽  
Huanjun Liu ◽  
Susan L. Ustin ◽  
Xinle Zhang
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Satellite Data ◽  
Maize Yield ◽  
Environmental Data ◽  
Yield Prediction ◽  
Climate Data ◽  
Multiple Sources ◽  
Adaptive Boosting ◽  
Source Data

Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.

scholarly journals Robustly forecasting maize yields in Tanzania based on climatic predictors

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Rahel Laudien ◽  
Bernhard Schauberger ◽  
David Makowski ◽  
Christoph Gornott
Keyword(s):  
Agricultural Development ◽  
Mean Squared Error ◽  
Global Climate ◽  
National Level ◽  
Maize Yield ◽  
Weather Data ◽  
Efficiency Coefficient ◽  
Climate Data ◽  
Yield Data ◽  
Out Of Sample

AbstractSeasonal yield forecasts are important to support agricultural development programs and can contribute to improved food security in developing countries. Despite their importance, no operational forecasting system on sub-national level is yet in place in Tanzania. We develop a statistical maize yield forecast based on regional yield statistics in Tanzania and climatic predictors, covering the period 2009–2019. We forecast both yield anomalies and absolute yields at the sub-national scale about 6 weeks before the harvest. The forecasted yield anomalies (absolute yields) have a median Nash–Sutcliffe efficiency coefficient of 0.72 (0.79) in the out-of-sample cross validation, which corresponds to a median root mean squared error of 0.13 t/ha for absolute yields. In addition, we perform an out-of-sample variable selection and produce completely independent yield forecasts for the harvest year 2019. Our study is potentially applicable to other countries with short time series of yield data and inaccessible or low quality weather data due to the usage of only global climate data and a strict and transparent assessment of the forecasting skill.

scholarly journals Reconstructing snow avalanches in the Southeastern Pyrenees

2009 ◽  
Vol 9 (5) ◽  
pp. 1599-1612 ◽  
Author(s):  
E. Muntán ◽  
C. García ◽  
P. Oller ◽  
G. Martí ◽  
A. García ◽  
...  
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Snow Avalanche ◽  
Weather Data ◽  
Snow Avalanches ◽  
Recent Past ◽  
Regional Study ◽  
Air Mass ◽  
Past Event

Abstract. A regional study of snow avalanche processes was undertaken in the SE Pyrenees. Dendrogeomorphology was used to date and reconstruct large-scale snow avalanche events that occurred in the last four decades. Dendrochronological analyses yielded the dates of nine winters when avalanches occurred in the recent past in six studied avalanche paths. Some of these avalanches were already known, but others had not been documented. In one case, the existing avalanche path map was improved with the dendrogeomorphological information of a larger past event. As a result of the dendrogeomorphological analyses, evidence for three regional-scale major avalanche years was identified in the SE Pyrenees from 1971 to 2004: 1971–1972, 1995–1996 and 2002–2003. The specific synoptic atmospheric situations and the most likely nivometeorological and snowpack conditions that released these major avalanches were determined using weather data for the seasons of major avalanche releases. In 1971–1972 the snow avalanche episode was characterized by a deep trough crossing the Pyrenees. In 1995–1996 a variety of meteorological situations produced several episodes of major avalanches. In 2002–2003 the more significant of two episodes was attributed to a north advection pumping an arctic air mass over the Pyrenees. The 1995–1996 avalanche season proved to be the most notable in the four past decades in the Pyrenees.

Polytope: Serving ECMWFs Big Weather Data

2020 ◽  
Author(s):  
James Hawkes ◽  
Nicolau Manubens ◽  
Emanuele Danovaro ◽  
John Hanley ◽  
Stephan Siemen ◽  
...  
Keyword(s):  
Spatial Data ◽  
High Performance ◽  
Large Scale ◽  
Data Extraction ◽  
Weather Data ◽  
Primary Data ◽  
Climate Data ◽  
Raw Data ◽  
Horizon 2020 ◽  
Weather And Climate

<div>Every day, ECMWF produces ~120TiB of raw weather data, represented as a six-dimensional dataset. This data is used to produce approximately 30TiB of user-defined products, which are disseminated worldwide. The raw data is also stored in the world's largest meteorological archive (MARS), currently holding over 300 PiB of primary data -- which is also served around the world on demand. As the resolution of ECMWFs global weather models increase over the next few years, the amount of raw data produced per day will increase into the petabytes, and the distribution of products and archived data becomes impossible. In-situ, on-the-fly data extraction and processing are required to sustain and increase the accessibility of ECMWFs big weather data.</div><div> </div><div>To meet these requirements, ECMWF is developing Polytope -- an open-source service which allows users to request arbitrary n-dimensional stencils ("polytopes") of data from highly-structured n-dimensional datasets. The data extraction is performed server-side (collocated with the data), allowing for large data reduction prior to transmission and less complexity for the user. For example, a user could request a polytope describing a flight path -- simultaneously crossing temporal and spatial axes. Polytope will return just a few bytes of data rather than large structured arrays of geo-spatial data which must be further post-processed by the user.</div><div> </div><div>Polytope is being partly developed under LEXIS, an EU-funded Horizon 2020 project which focuses on large-scale HPC & cloud workflows. The emphasis of LEXIS is on how HPC and cloud systems interact; how they can share data; and methods to compose workflows of tasks running on both cloud and HPC systems. Polytope will be used to provide a cross-centre weather and climate data API which connects to multiple high-performance data sources across Europe, and serves multiple cloud environments with this data.</div><div> </div><div>This poster will present the early developments and future vision of Polytope. It will also illustrate how it is used within the LEXIS project to enable complex weather and climate workflows, involving global forecasts, regional forecasts and cloud-based simulations.</div>

scholarly journals Remote Sensing-Based Quantification of the Summer Maize Yield Gap Induced by Suboptimum Sowing Dates over North China Plain

2021 ◽  
Vol 13 (18) ◽  
pp. 3582 ◽  
Author(s):  
Sha Zhang ◽  
Yun Bai ◽  
Jiahua Zhang
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
North China Plain ◽  
North China ◽  
Regional Scale ◽  
Maize Yield ◽  
Yield Potential ◽  
Management Information ◽  
Summer Maize ◽  
Yield Gap

Estimating yield potential (Yp) and quantifying the contribution of suboptimum field managements to the yield gap (Yg) of crops are important for improving crop yield effectively. However, achieving this goal on a regional scale remains difficult because of challenges in collecting field management information. In this study, we retrieved crop management information (i.e., emerging stage information and a surrogate of sowing date (SDT)) from a remote sensing (RS) vegetation index time series. Then, we developed a new approach to quantify maize Yp, total Yg, and the suboptimum SDT-induced Yg (Yg0) using a process-based RS-driven crop yield model for maize (PRYM–Maize), which was developed in our previous study. PRYM–Maize and the newly developed method were used over the North China Plain (NCP) to estimate Ya, Yp, Yg, and Yg0 of summer maize. Results showed that PRYM–Maize outputs reasonable estimates for maize yield over the NCP, with correlations and root mean standard deviation of 0.49 ± 0.24 and 0.88 ± 0.14 t hm−2, respectively, for modeled annual maize yields versus the reference value for each year over the period 2010 to 2015 on a city level. Yp estimated using our new method can reasonably capture the spatial variations in site-level estimates from crop growth models in previous literature. The mean annual regional Yp of 2010–2015 was estimated to be 11.99 t hm−2, and a Yg value of 5.4 t hm−2 was found between Yp and Ya on a regional scale. An estimated 29–42% of regional Yg in each year (2010–2015) was induced by suboptimum SDT. Results also show that not all Yg0 was persistent over time. Future studies using high spatial-resolution RS images to disaggregate Yg0 into persistent and non-persistent components on a small scale are required to increase maize yield over the NCP.

scholarly journals Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2576
Author(s):  
Martin Engen ◽  
Erik Sandø ◽  
Benjamin Lucas Oscar Sjølander ◽  
Simon Arenberg ◽  
Rashmi Gupta ◽  
...  
Keyword(s):  
Neural Networks ◽  
Crop Yield ◽  
Regional Scale ◽  
Ground Truth ◽  
Weather Data ◽  
Yield Prediction ◽  
Temporal Data ◽  
Yield Production ◽  
Multi Temporal ◽  
Farm Scale

Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weather data, farm data, grain delivery data, and cadastre-specific data. We introduce a deep hybrid neural network model to train this multi-temporal data. This model combines the features of convolutional layers and recurrent neural networks to predict farm-scale crop yield production across Norway. The proposed model could efficiently make the target predictions with the mean absolute error of 76 kg per 1000 m2. In conclusion, the reusable farm-scale multi-temporal crop yield dataset and the proposed novel model could meet the actual requirements for the prediction targets in this paper, providing further valuable insights for the research community.

Prediction of aircraft estimated time of arrival using machine learning methods

2021 ◽  
pp. 1-15
Author(s):  
O. Basturk ◽  
C. Cetek
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Time Of Arrival ◽  
Learning Models ◽  
Trajectory Data ◽  
Different Sources ◽  
Machine Learning Models

ABSTRACT In this study, prediction of aircraft Estimated Time of Arrival (ETA) is proposed using machine learning algorithms. Accurate prediction of ETA is important for management of delay and air traffic flow, runway assignment, gate assignment, collaborative decision making (CDM), coordination of ground personnel and equipment, and optimisation of arrival sequence etc. Machine learning is able to learn from experience and make predictions with weak assumptions or no assumptions at all. In the proposed approach, general flight information, trajectory data and weather data were obtained from different sources in various formats. Raw data were converted to tidy data and inserted into a relational database. To obtain the features for training the machine learning models, the data were explored, cleaned and transformed into convenient features. New features were also derived from the available data. Random forests and deep neural networks were used to train the machine learning models. Both models can predict the ETA with a mean absolute error (MAE) less than 6min after departure, and less than 3min after terminal manoeuvring area (TMA) entrance. Additionally, a web application was developed to dynamically predict the ETA using proposed models.

scholarly journals Assessing Annual Actual Evapotranspiration Based on Climate, Topography and Soil in Natural and Agricultural Ecosystems

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 20
Author(s):  
Kleoniki Demertzi ◽  
Vassilios Pisinaras ◽  
Emanuel Lekakis ◽  
Evangelos Tziritis ◽  
Konstantinos Babakos ◽  
...  
Keyword(s):  
Water Balance ◽  
Large Scale ◽  
Hydrological Model ◽  
Actual Evapotranspiration ◽  
Irrigated Agriculture ◽  
Climate Data ◽  
Natural Ecosystems ◽  
Loading Effects ◽  
Very High ◽  
Of Greece

Simple formulas for estimating annual actual evapotranspiration (AET) based on annual climate data are widely used in large scale applications. Such formulas do not have distinct compartments related to topography, soil and irrigation, and for this reason may be limited in basins with high slopes, where runoff is the dominant water balance component, and in basins where irrigated agriculture is dominant. Thus, a simplistic method for assessing AET in both natural ecosystems and agricultural systems considering the aforementioned elements is proposed in this study. The method solves AET through water balance based on a set of formulas that estimate runoff and percolation. These formulas are calibrated by the results of the deterministic hydrological model GLEAMS (Groundwater Loading Effects of Agricultural Management Systems) for a reference surface. The proposed methodology is applied to the country of Greece and compared with the widely used climate-based methods of Oldekop, Coutagne and Turk. The results show that the proposed methodology agrees very well with the method of Turk for the lowland regions but presents significant differences in places where runoff is expected to be very high (sloppy areas and areas of high rainfall, especially during December–February), suggesting that the proposed method performs better due to its runoff compartment. The method can also be applied in a single application considering irrigation only for the irrigated lands to more accurately estimate AET in basins with a high percentage of irrigated agriculture.

scholarly journals Classification of Recreation Opportunity Spectrum Using Night Lights for Evidence of Humans and POI Data for Social Setting

2021 ◽  
Vol 13 (14) ◽  
pp. 7782
Author(s):  
Wenjing Zeng ◽  
Yongde Zhong ◽  
Dali Li ◽  
Jinyang Deng
Keyword(s):  
Large Scale ◽  
Regional Scale ◽  
Hunan Province ◽  
Social Settings ◽  
Recreation Opportunity Spectrum ◽  
City District ◽  
Night Lights ◽  
Recreational Resources ◽  
Metropolitan Counties ◽  
Land Types

The recreation opportunity spectrum (ROS) has been widely recognized as an effective tool for the inventory and planning of outdoor recreational resources. However, its applications have been primarily focused on forest-dominated settings with few studies being conducted on all land types at a regional scale. The creation of a ROS is based on physical, social, and managerial settings, with the physical setting being measured by three criteria: remoteness, size, and evidence of humans. One challenge to extending the ROS to all land types on a large scale is the difficulty of quantifying the evidence of humans and social settings. Thus, this study, for the first time, developed an innovative approach that used night lights as a proxy for evidence of humans and points of interest (POI) for social settings to generate an automatic ROS for Hunan Province using Geographic Information System (GIS) spatial analysis. The whole province was classified as primitive (2.51%), semi-primitive non-motorized (21.33%), semi-primitive motorized (38.60%), semi-developed natural (30.99%), developed natural (5.61%), and highly developed (0.96%), which was further divided into three subclasses: large-natural (0.63%), small natural (0.27%), and facilities (0.06%). In order to implement the management and utilization of natural recreational resources in Hunan Province at the county (city, district) level, the province’s 122 counties (cities, districts) were categorized into five levels based on the ROS factor dominance calculated at the county and provincial levels. These five levels include key natural recreational counties (cities, districts), general natural recreational counties (cities, districts), rural counties (cities, districts), general metropolitan counties (cities, districts), and key metropolitan counties (cities, districts), with the corresponding numbers being 8, 21, 50, 24, and 19, respectively.

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