scholarly journals In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt

2020 ◽  
Author(s):  
Vijaya R. Joshi ◽  
Maciej J. Kazula ◽  
Jeffrey A. Coulter ◽  
Seth L. Naeve ◽  
Axel Garcia y Garcia
Keyword(s):  
Air Temperature ◽  
Growth And Yield ◽  
Weather Data ◽  
Corn Belt ◽  
Support Vector ◽  
Relative Importance ◽  
Yield Estimation ◽  
Support Tool ◽  
The Us ◽  
Total Study Area

AbstractWeather conditions regulate the growth and yield of crops, especially in rain-fed agricultural systems. This study evaluated the use and relative importance of readily available weather data to develop yield estimation models for maize and soybean in the US central Corn Belt. Total rainfall (Rain), average air temperature (Tavg), and the difference between maximum and minimum air temperature (Tdiff) at weekly, biweekly, and monthly timescales from May to August were used to estimate county-level maize and soybean grain yields for Iowa, Illinois, Indiana, and Minnesota. Step-wise multiple linear regression (MLR), general additive (GAM), and support vector machine (SVM) models were trained with Rain, Tavg, and with/without Tdiff. For the total study area and at individual state level, SVM outperformed other models at all temporal levels for both maize and soybean. For maize, Tavg and Tdiff during July and August, and Rain during June and July, were relatively more important whereas for soybean, Tavg in June and Tdiff and Rain during August were more important. The SVM model with weekly Rain and Tavg estimated the overall maize yield with a root mean square error (RMSE) of 591 kg ha−1 (4.9% nRMSE) and soybean yield with a RMSE of 205 kg ha−1 (5.5% nRMSE). Inclusion of Tdiff in the model considerably improved yield estimation for both crops; however, the magnitude of improvement varied with the model and temporal level of weather data. This study shows the relative importance of weather variables and reliable yield estimation of maize and soybean from readily available weather data to develop a decision support tool in the US central Corn Belt.

scholarly journals Estimation of Daily Air Temperature Based on MODIS Land Surface Temperature Products over the Corn Belt in the US

2015 ◽  
Vol 7 (1) ◽  
pp. 951-970 ◽  
Author(s):  
Linglin Zeng ◽  
Brian Wardlow ◽  
Tsegaye Tadesse ◽  
Jie Shan ◽  
Michael Hayes ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Corn Belt ◽  
Modis Land Surface Temperature ◽  
The Us

A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level

2019 ◽  
Vol 26 (3) ◽  
pp. 1754-1766 ◽  
Author(s):  
Hao Jiang ◽  
Hao Hu ◽  
Renhai Zhong ◽  
Jinfan Xu ◽  
Jialu Xu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Geospatial Data ◽  
Corn Belt ◽  
Corn Yield ◽  
Learning Approach ◽  
County Level ◽  
Yield Estimation ◽  
The Us

Learning main drivers of crop dynamics and production in Europe

2020 ◽  
Author(s):  
Anna Mateo Sanchis ◽  
Maria Piles ◽  
Julia Amorós López ◽  
Jordi Muñoz Marí ◽  
Gustau Camps Valls
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Vegetation Indices ◽  
Agricultural Landscapes ◽  
Sensor Data ◽  
Corn Belt ◽  
World Population ◽  
Yield Estimation ◽  
The Us ◽  
Homogeneous Area

<p>An expanding world population combined with challenges brought by climate change pose totally new scenarios for managing agricultural fields and crop production. In the last decades, a variety of ground-based, modeled, and Earth observation (EO) data have been used to characterize crop dynamics and, ultimately, estimate yield. Typically, optical vegetation indices and, in particular, metrics like their maximum peak or integral during the growing season are exploited to estimated crop yield. Also, most studies are focused on large areas with homogeneous agricultural landscapes in which cultivation/production is centred in a unique main crop (e.g. the U.S. Corn Belt or the Indian Wheat Belt). </p><p>In this study, we study the transportability of machine learning models for crop yield estimation across different regions and the relative relevance of agro-ecological drivers (input features). We use a previous methodology presented in [1] that synergistically combined optical and microwave vegetation data for crop yield prediction. We apply this methodology, which was trained in the homogeneous area of the US Corn Belt, to the highly heterogeneous agricultural landscapes across Europe. The fragmented and diverse European agro-ecosystems poses a greater challenge for the combination of multi-sensor data, and we need to establish first which is the set of variables providing the best skill for yield estimation of the main crops grown in Europe (corn, barley and wheat) under this new scenario. Subsequently, we study whether these variables are also able to capture potential disruptions on crop dynamics deriving from extreme events and their influence in final crop production. </p><p>[1] Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation. Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay and Gustau Camps-Valls. Remote Sensing of Environment 234:111460, 2019.</p>

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals Estimation of Cry3Bb1 resistance allele frequency in field populations of western corn rootworm using a genetic marker

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alan Willse ◽  
Lex Flagel ◽  
Graham Head
Keyword(s):  
Allele Frequency ◽  
Western Corn Rootworm ◽  
Allele Frequencies ◽  
Chromosome 8 ◽  
Corn Belt ◽  
Corn Rootworm ◽  
Resistance Allele ◽  
Response To Selection ◽  
The Us ◽  
Causal Allele

Abstract Following the discovery of western corn rootworm (WCR; Diabrotica virgifera virgifera) populations resistant to the Bacillus thuringiensis (Bt) protein Cry3Bb1, resistance was genetically mapped to a single locus on WCR chromosome 8 and linked SNP markers were shown to correlate with the frequency of resistance among field-collected populations from the US Corn Belt. The purpose of this paper is to further investigate the relationship between one of these resistance-linked markers and the causal resistance locus. Using data from laboratory bioassays and field experiments, we show that one allele of the resistance-linked marker increased in frequency in response to selection, but was not perfectly linked to the causal resistance allele. By coupling the response to selection data with a genetic model of the linkage between the marker and the causal allele, we developed a model that allowed marker allele frequencies to be mapped to causal allele frequencies. We then used this model to estimate the resistance allele frequency distribution in the US Corn Belt based on collections from 40 populations. These estimates suggest that chromosome 8 Cry3Bb1 resistance allele frequency was generally low (<10%) for 65% of the landscape, though an estimated 13% of landscape has relatively high (>25%) resistance allele frequency.

scholarly journals Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1377
Author(s):  
Weifang Shi ◽  
Nan Wang ◽  
Aixuan Xin ◽  
Linglan Liu ◽  
Jiaqi Hou ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Path Analysis ◽  
Temperature Variation ◽  
Direct Effect ◽  
Air Temperature ◽  
Urban Areas ◽  
Support Vector ◽  
Urban Air ◽  
Air Temperatures

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

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