Macro-scale influence of climate on crop production in the Fitzroy catchment of Central Queensland

1999 ◽  
Vol 50 (4) ◽  
pp. 529
Author(s):  
F. M. Amirul Islam ◽  
Saleh A. Wasimi ◽  
Graham R. Wood
Keyword(s):  
Crop Yield ◽  
General Circulation ◽  
Crop Production ◽  
Climatic Variability ◽  
Climatic Conditions ◽  
Response Variable ◽  
Explanatory Variables ◽  
Genetic Gains ◽  
Macro Scale ◽  
Expected Values

When the dynamics of a system is too complex to be analytically modelled, it has been found useful to assume that expected values of explanatory variables generate expected values of the response variable, and hence, deviations from the expected value of the response variable can be modelled by a Linear Perturbation Model (LPM) of the explanatory variables. This method is used in this study to develop a technique to update crop forecasts where climate is a major factor in crop production. The study is important because modern cultivars, which are the result of genetic gains, are sensitive to climatic variability, and recent studies with general circulation models suggest that one of the consequences of an increase in greenhouse gases may be greater variability in the climate of a region. The usefulness of the LPM technique in the study of agriculture–climate relationships is tested through application to the Fitzroy catchment in Central Queensland. Since no reported climatic change is yet occurring in the region, the expected values for climatic conditions are obtained through averaging. By contrast, the expected values of crop yield are obtained from trend analysis; such trends are mainly attributable to genetic gains in the recent past. Three crops (wheat, barley, and sunflower) have been studied. Deviations (or perturbations) in crop yields are related, in the framework of LPM, to deviations in minimum, maximum, and average values of rainfall, temperature, and humidity at planting, flowering, and harvesting time. The most significant climatic factors affecting deviations in crop yield are identified. Regression models are developed which are capable of filtering and updating crop forecasts due to any unexpected climatic conditions, assuming consistent genetic trends and management practices.

scholarly journals Agriculture Data Analytics in Crop Yield Estimation: A Critical Review

2018 ◽  
Vol 12 (3) ◽  
pp. 1087 ◽  
Author(s):  
B.M. Sagar ◽  
Cauvery N K
Keyword(s):  
Big Data ◽  
Crop Yield ◽  
Data Analytics ◽  
Crop Production ◽  
Big Data Analytics ◽  
Agricultural Practices ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Main Challenge ◽  
Crop Health

<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of          Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics.  Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

scholarly journals Crop yield prediction integrating genotype and weather variables using deep learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252402
Author(s):  
Johnathon Shook ◽  
Tryambak Gangopadhyay ◽  
Linjiang Wu ◽  
Baskar Ganapathysubramanian ◽  
Soumik Sarkar ◽  
...  
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Short Term Memory ◽  
Time Windows ◽  
Climatic Conditions ◽  
Support Vector ◽  
Yield Prediction ◽  
Lasso Regression ◽  
Important Time ◽  
Interpretable Models

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.

scholarly journals Deep Neural Network Model for Proficient Crop Yield Prediction

2021 ◽  
Vol 309 ◽  
pp. 01031
Author(s):  
K. Pravallika ◽  
G. Karuna ◽  
K. Anuradha ◽  
V. Srilakshmi
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Climatic Conditions ◽  
Yield Prediction ◽  
Knowledge Process ◽  
Fundamental Understanding ◽  
Yield Forecast ◽  
Learning Machine ◽  
Crop Yield Forecasting ◽  
And Storage

Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.

scholarly journals Application of Machine learning in Crop Yield Prediction of Finger Millet using Multiple Linear Regression

2020 ◽  
pp. 280-284
Author(s):  
Tarun Mourya. S S ◽  
S. Nagini ◽  
Anne Slagha
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Crop Yield ◽  
Crop Production ◽  
Finger Millet ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Climatic Conditions ◽  
The Given

Agriculture is the mainstay of the Indian economy and it is important to enhance the production with the help of technology. Crop production is a complex phenomenon that is influenced by various parameters like climatic conditions, fertilizers, production, rainfall, etc. In the present study Machine learning is used to predict the crop yield of finger millet using multiple linear regression analysis. Multiple linear regression is considered as a model for prediction and the accuracy of the model for the given data is significantly high when compared to other models. Object oriented Python is used and packages like NumPy and Pandas are utilized for performing operations and data analysis respectively. The main advantage of the research is the prediction of the approximate crop yield well ahead of its harvest, which would help the farmers in taking appropriate measures in crop cultivation, marketing and storage. Such predictions will also help the associated industries for planning the logistics of their business.

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 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.

scholarly journals How Can We Realize Sustainable Development Goals in Rocky Desertified Regions by Enhancing Crop Yield with Reduction of Environmental Risks?

2021 ◽  
Vol 13 (9) ◽  
pp. 1614
Author(s):  
Boyi Liang ◽  
Timothy A. Quine ◽  
Hongyan Liu ◽  
Elizabeth L. Cressey ◽  
Ian Bateman
Keyword(s):  
Sustainable Development ◽  
Crop Yield ◽  
Crop Production ◽  
Sustainable Development Goals ◽  
Positive Impact ◽  
Total Yield ◽  
Maximum Yield ◽  
Guizhou Province ◽  
Crop Species ◽  
Development Goals

To meet the sustainable development goals in rocky desertified regions like Guizhou Province in China, we should maximize the crop yield with minimal environmental costs. In this study, we first calculated the yield gap for 6 main crop species in Guizhou Province and evaluated the quantitative relationships between crop yield and influencing variables utilizing ensembled artificial neural networks. We also tested the influence of adjusting the quantity of local fertilization and irrigation on crop production in Guizhou Province. Results showed that the total yield of the selected crops had, on average, reached over 72.5% of the theoretical maximum yield. Increasing irrigation tended to be more consistently effective at increasing crop yield than additional fertilization. Conversely, appropriate reduction of fertilization may even benefit crop yield in some regions, simultaneously resulting in significantly higher fertilization efficiency with lower residuals in the environment. The total positive impact of continuous intensification of irrigation and fertilization on most crop species was limited. Therefore, local stakeholders are advised to consider other agricultural management measures to improve crop yield in this region.

scholarly journals The Pitfalls of Relating Weeds, Herbicide Use, and Crop Yield: Don't Fall Into the Trap! A Critical Review

2020 ◽  
Vol 2 ◽  
Author(s):  
Nathalie Colbach ◽  
Sandrine Petit ◽  
Bruno Chauvel ◽  
Violaine Deytieux ◽  
Martin Lechenet ◽  
...  
Keyword(s):  
Crop Yield ◽  
Weed Management ◽  
Crop Production ◽  
Yield Loss ◽  
Crop Yields ◽  
Cropping Systems ◽  
Crop Productivity ◽  
Arable Farming ◽  
Herbicide Use ◽  
The Impact

The growing recognition of the environmental and health issues associated to pesticide use requires to investigate how to manage weeds with less or no herbicides in arable farming while maintaining crop productivity. The questions of weed harmfulness, herbicide efficacy, the effects of herbicide use on crop yields, and the effect of reducing herbicides on crop production have been addressed over the years but results and interpretations often appear contradictory. In this paper, we critically analyze studies that have focused on the herbicide use, weeds and crop yield nexus. We identified many inconsistencies in the published results and demonstrate that these often stem from differences in the methodologies used and in the choice of the conceptual model that links the three items. Our main findings are: (1) although our review confirms that herbicide reduction increases weed infestation if not compensated by other cultural techniques, there are many shortcomings in the different methods used to assess the impact of weeds on crop production; (2) Reducing herbicide use rarely results in increased crop yield loss due to weeds if farmers compensate low herbicide use by other efficient cultural practices; (3) There is a need for comprehensive studies describing the effect of cropping systems on crop production that explicitly include weeds and disentangle the impact of herbicides from the effect of other practices on weeds and on crop production. We propose a framework that presents all the links and feed-backs that must be considered when analyzing the herbicide-weed-crop yield nexus. We then provide a number of methodological recommendations for future studies. We conclude that, since weeds are causing yield loss, reduced herbicide use and maintained crop productivity necessarily requires a redesign of cropping systems. These new systems should include both agronomic and biodiversity-based levers acting in concert to deliver sustainable weed management.

scholarly journals The interaction of seasonal rainfall and nitrogen fertiliser rate on soil N2O emission, total N loss and crop yield of dryland sorghum and sunflower grown on sub-tropical Vertosols

Soil Research ◽  
2016 ◽  
Vol 54 (5) ◽  
pp. 604 ◽  
Author(s):  
G. D. Schwenke ◽  
B. M. Haigh
Keyword(s):  
Crop Yield ◽  
Crop Production ◽  
Field Experiments ◽  
N2o Emission ◽  
N2o Emissions ◽  
N Loss ◽  
N Losses ◽  
Total N ◽  
Tropical Regions ◽  
The Impact

Summer crop production on slow-draining Vertosols in a sub-tropical climate has the potential for large emissions of soil nitrous oxide (N2O) from denitrification of applied nitrogen (N) fertiliser. While it is well established that applying N fertiliser will increase N2O emissions above background levels, previous research in temperate climates has shown that increasing N fertiliser rates can increase N2O emissions linearly, exponentially or not at all. Little such data exists for summer cropping in sub-tropical regions. In four field experiments at two locations across two summers, we assessed the impact of increasing N fertiliser rate on both soil N2O emissions and crop yield of grain sorghum (Sorghum bicolor L.) or sunflower (Helianthus annuus L.) in Vertosols of sub-tropical Australia. Rates of N fertiliser, applied as urea at sowing, included a nil application, an optimum N rate and a double-optimum rate. Daily N2O fluxes ranged from –3.8 to 2734g N2O-Nha–1day–1 and cumulative N2O emissions ranged from 96 to 6659g N2O-Nha–1 during crop growth. Emissions of N2O increased with increased N fertiliser rates at all experimental sites, but the rate of N loss was five times greater in wetter-than-average seasons than in drier conditions. For two of the four experiments, periods of intense rainfall resulted in N2O emission factors (EF, percent of applied N emitted) in the range of 1.2–3.2%. In contrast, the EFs for the two drier experiments were 0.41–0.56% with no effect of N fertiliser rate. Additional 15N mini-plots aimed to determine whether N fertiliser rate affected total N lost from the soil–plant system between sowing and harvest. Total 15N unaccounted was in the range of 28–45% of applied N and was presumed to be emitted as N2O+N2. At the drier site, the ratio of N2 (estimated by difference)to N2O (measured) lost was a constant 43%, whereas the ratio declined from 29% to 12% with increased N fertiliser rate for the wetter experiment. Choosing an N fertiliser rate aimed at optimum crop production mitigates potentially high environmental (N2O) and agronomic (N2+N2O) gaseous N losses from over-application, particularly in seasons with high intensity rainfall occurring soon after fertiliser application.

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