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>

scholarly journals Increasing carbon footprint of grain crop production in the US Western Corn Belt

2018 ◽  
Vol 13 (12) ◽  
pp. 124007 ◽  
Author(s):  
Chaoqun Lu ◽  
Zhen Yu ◽  
Hanqin Tian ◽  
David A Hennessy ◽  
Hongli Feng ◽  
...  
Keyword(s):  
Carbon Footprint ◽  
Crop Production ◽  
Corn Belt ◽  
The Us ◽  
Grain Crop

Closing Yield Gaps: Consequences for the Global Food Supply, Environmental Quality & Food Security

Daedalus ◽  
2015 ◽  
Vol 144 (4) ◽  
pp. 45-56 ◽  
Author(s):  
Nathaniel D. Mueller ◽  
Seth Binder
Keyword(s):  
Food Security ◽  
Crop Yield ◽  
Food Supply ◽  
Crop Production ◽  
Crop Yields ◽  
Agricultural Landscapes ◽  
Environmental Benefits ◽  
Potential Contribution ◽  
Yield Gap ◽  
Yield Gaps

The social, economic, and environmental costs of feeding a burgeoning and increasingly affluent human population will depend, in part, on how we increase crop production on under-yielding agricultural landscapes, and by how much. Such areas have a “yield gap” between the crop yields they achieve and the crop yields that could be achieved under more intensive management. Crop yield gaps have received increased attention in recent years due to concerns over land scarcity, stagnating crop yield trends in some important agricultural areas, and large projected increases in food demand. Recent analyses of global data sets and results from field trials have improved our understanding of where yield gaps exist and their potential contribution to increasing the food supply. Achieving yield gap closure is a complex task: while agronomic approaches to closing yield gaps are generally well-known, a variety of social, political, and economic factors allow them to persist. The degree to which closing yield gaps will lead to greater food security and environmental benefits remains unclear, and will be strongly influenced by the particular strategies adopted.

Nitrous oxide emissions from agricultural soils challenge climate sustainability in the US Corn Belt

2021 ◽  
Vol 118 (46) ◽  
pp. e2112108118
Author(s):  
Nathaniel C. Lawrence ◽  
Carlos G. Tenesaca ◽  
Andy VanLoocke ◽  
Steven J. Hall
Keyword(s):  
Nitrous Oxide ◽  
Soil Carbon ◽  
Agricultural Soils ◽  
Agricultural Landscapes ◽  
Nitrous Oxide Emissions ◽  
Corn Belt ◽  
Agricultural Practice ◽  
Mean Values ◽  
The Us ◽  
Soil Emissions

Agricultural landscapes are the largest source of anthropogenic nitrous oxide (N2O) emissions, but their specific sources and magnitudes remain contested. In the US Corn Belt, a globally important N2O source, in-field soil emissions were reportedly too small to account for N2O measured in the regional atmosphere, and disproportionately high N2O emissions from intermittent streams have been invoked to explain the discrepancy. We collected 3 y of high-frequency (4-h) measurements across a topographic gradient, including a very poorly drained (intermittently flooded) depression and adjacent upland soils. Mean annual N2O emissions from this corn–soybean rotation (7.8 kg of N2O–N ha−1⋅y−1) were similar to a previous regional top-down estimate, regardless of landscape position. Synthesizing other Corn Belt studies, we found mean emissions of 5.6 kg of N2O–N ha−1⋅y−1 from soils with similar drainage to our transect (moderately well-drained to very poorly drained), which collectively comprise 60% of corn–soybean-cultivated soils. In contrast, strictly well-drained soils averaged only 2.3 kg of N2O–N ha−1⋅y−1. Our results imply that in-field N2O emissions from soils with moderately to severely impaired drainage are similar to regional mean values and that N2O emissions from well-drained soils are not representative of the broader Corn Belt. On the basis of carbon dioxide equivalents, the warming effect of direct N2O emissions from our transect was twofold greater than optimistic soil carbon gains achievable from agricultural practice changes. Despite the recent focus on soil carbon sequestration, addressing N2O emissions from wet Corn Belt soils may have greater leverage in achieving climate sustainability.

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

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 Sustainable Agriculture through Multidisciplinary Seed Nanopriming: Prospects of Opportunities and Challenges

Cells ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 2428
Author(s):  
Amruta Shelar ◽  
Ajay Vikram Singh ◽  
Romi Singh Maharjan ◽  
Peter Laux ◽  
Andreas Luch ◽  
...  
Keyword(s):  
Sustainable Agriculture ◽  
Crop Yield ◽  
Crop Production ◽  
Developmental Stages ◽  
Hormone Secretion ◽  
Seed Priming ◽  
Seed Vigor ◽  
World Population ◽  
Sustainable Food ◽  
Seed Technology

The global community decided in 2015 to improve people’s lives by 2030 by setting 17 global goals for sustainable development. The second goal of this community was to end hunger. Plant seeds are an essential input in agriculture; however, during their developmental stages, seeds can be negatively affected by environmental stresses, which can adversely affect seed vigor, seedling establishment, and crop production. Seeds resistant to high salinity, droughts and climate change can result in higher crop yield. The major findings suggested in this review refer nanopriming as an emerging seed technology towards sustainable food amid growing demand with the increasing world population. This novel growing technology could influence the crop yield and ensure the quality and safety of seeds, in a sustainable way. When nanoprimed seeds are germinated, they undergo a series of synergistic events as a result of enhanced metabolism: modulating biochemical signaling pathways, trigger hormone secretion, reduce reactive oxygen species leading to improved disease resistance. In addition to providing an overview of the challenges and limitations of seed nanopriming technology, this review also describes some of the emerging nano-seed priming methods for sustainable agriculture, and other technological developments using cold plasma technology and machine learning.

Analysis of Crop Yield Prediction of Kharif & Rabi Jowar Crops Using Data Mining Techniques

2017 ◽  
Vol 7 (11) ◽  
pp. 79
Author(s):  
Sujata Mulik
Keyword(s):  
Data Mining ◽  
Crop Yield ◽  
Crop Production ◽  
Climatic Factors ◽  
Crop Productivity ◽  
Yield Prediction ◽  
Data Mining Techniques ◽  
Agriculture Sector ◽  
Using Data ◽  
Rabi Crops

Agriculture sector in India is facing rigorous problem to maximize crop productivity. More than 60 percent of the crop still depends on climatic factors like rainfall, temperature, humidity. This paper discusses the use of various Data Mining applications in agriculture sector. Data Mining is used to solve various problems in agriculture sector. It can be used it to solve yield prediction.  The problem of yield prediction is a major problem that remains to be solved based on available data. Data mining techniques are the better choices for this purpose. Different Data Mining techniques are used and evaluated in agriculture for estimating the future year's crop production. In this paper we have focused on predicting crop yield productivity of kharif & Rabi Crops. 

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 Agro-Nanotechnology as an Emerging Field: A Novel Sustainable Approach for Improving Plant Growth by Reducing Biotic Stress

2021 ◽  
Vol 11 (5) ◽  
pp. 2282
Author(s):  
Masudulla Khan ◽  
Azhar U. Khan ◽  
Mohd Abul Hasan ◽  
Krishna Kumar Yadav ◽  
Marina M. C. Pinto ◽  
...  
Keyword(s):  
Plant Growth ◽  
Crop Yield ◽  
Biotic Stress ◽  
Crop Production ◽  
Plant Disease ◽  
Yield Loss ◽  
Growth Parameters ◽  
Plant Diseases ◽  
High Yield ◽  
Plant Disease Management

In the present era, the global need for food is increasing rapidly; nanomaterials are a useful tool for improving crop production and yield. The application of nanomaterials can improve plant growth parameters. Biotic stress is induced by many microbes in crops and causes disease and high yield loss. Every year, approximately 20–40% of crop yield is lost due to plant diseases caused by various pests and pathogens. Current plant disease or biotic stress management mainly relies on toxic fungicides and pesticides that are potentially harmful to the environment. Nanotechnology emerged as an alternative for the sustainable and eco-friendly management of biotic stress induced by pests and pathogens on crops. In this review article, we assess the role and impact of different nanoparticles in plant disease management, and this review explores the direction in which nanoparticles can be utilized for improving plant growth and crop yield.

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