Gridded 1 km × 1 km emission inventory for paddy stubble burning emissions over north-west India constrained by measured emission factors of 77 VOCs and district-wise crop yield data

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production. Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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The Use of Crop Yield Autocorrelation Data as a Sustainable Approach to Adjust Agronomic Inputs

Sustainability ◽

10.3390/su13042362 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2362

Author(s):

Thomas M. Koutsos ◽

Georgios C. Menexes ◽

Andreas P. Mamolos

Keyword(s):

Decision Making ◽

Spatial Analysis ◽

Crop Yield ◽

Spatial Autocorrelation Analysis ◽

Autocorrelation Analysis ◽

Yield Data ◽

Site Specific ◽

Production Areas ◽

Short Time ◽

Monitor Data

Agricultural fields have natural within-field soil variations that can be extensive, are usually contiguous, and are not always traceable. As a result, in many cases, site-specific attention is required to adjust inputs and optimize crop performance. Researchers, such as agronomists, agricultural engineers, or economists and other scientists, have shown increased interest in performing yield monitor data analysis to improve farmers’ decision-making concerning the better management of the agronomic inputs in the fields, while following a much more sustainable approach. In this case, spatial analysis of crop yield data with the form of spatial autocorrelation analysis can be used as a practical sustainable approach to locate statistically significant low-production areas. The resulted insights can be used as prescription maps on the tractors to reduce overall inputs and farming costs. This aim of this work is to present the benefits of conducting spatial analysis of yield crop data as a sustainable approach. Current work proves that the implementation of this process is costless, easy to perform and provides a better understanding of the current agronomic needs for better decision-making within a short time, adopting a sustainable approach.

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Determination of black carbon, PM2.5, particle number and NOx emission factors from roadside measurements and their implications for emission inventory development

Atmospheric Environment ◽

10.1016/j.atmosenv.2018.05.042 ◽

2018 ◽

Vol 186 ◽

pp. 229-240 ◽

Cited By ~ 22

Author(s):

Patricia Krecl ◽

Admir Créso Targino ◽

Thiago Pereira Landi ◽

Matthias Ketzel

Keyword(s):

Black Carbon ◽

Emission Inventory ◽

Particle Number ◽

Emission Factors ◽

Nox Emission ◽

Inventory Development

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Geostatistical analysis of crop yield maps in a long term no tillage system

Bragantia ◽

10.1590/s0006-87052010000500003 ◽

2010 ◽

Vol 69 (suppl) ◽

pp. 9-18 ◽

Cited By ~ 3

Author(s):

Osvaldo Guedes Filho ◽

Sidney Rosa Vieira ◽

Marcio Koiti Chiba ◽

Célia Regina Grego

Keyword(s):

Crop Yield ◽

Crop Yields ◽

Spherical Model ◽

Exponential Model ◽

Spatial And Temporal Variability ◽

No Tillage ◽

Yield Data ◽

The North ◽

Tillage System

It is known, for a long time, that crop yields are not uniform at the field. In some places, it is possible to distinguish sites with both low and high yields even within the same area. This work aimed to evaluate the spatial and temporal variability of some crop yields and to identify potential zones for site specific management in an area under no-tillage system for 23 years. Data were analyzed from a 3.42 ha long term experimental area at the Centro Experimental Central of the Instituto Agronômico, located in Campinas, Sao Paulo State, Brazil. The crop yield data evaluated included the following crops: soybean, maize, lablab and triticale, and all of them were cultivated since 1985 and sampled at a regular grid of 302 points. Data were normalized and analyzed using descriptive statistics and geostatistical tools in order to demonstrate and describe the structure of the spatial variability. All crop yields showed high variability. All of them also showed spatial dependence and were fitted to the spherical model, except for the yield of the maize in 1999 productivity which was fitted to the exponential model. The north part of the area presented repeated high values of productivity in some years. There was a positive cross correlation amongst the productivity values, especially for the maize crops.

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Forecasting the Crop Yield Production in Trichy District Using Fuzzy C-Means Algorithm and Multilayer Perceptron (MLP)

International Journal of Knowledge and Systems Science ◽

10.4018/ijkss.2020070105 ◽

2020 ◽

Vol 11 (3) ◽

pp. 83-98

Author(s):

Geetha M. C. S. ◽

Elizabeth Shanthi I.

Keyword(s):

Data Mining ◽

Crop Yield ◽

Multilayer Perceptron ◽

Training Data ◽

Yield Data ◽

Economic Determinants ◽

Fuzzy C Means ◽

Yield Production ◽

Yield Forecast ◽

Fuzzy C Means Algorithm

The agricultural stock depends upon several factors like biological, seasonal, and economic determinants. The growers sustain a vital loss if they are not capable of predicting the variations in these circumstances. The uncertainty on crop yield can be predicted in a logical and mathematical way. The forecast is made based on the previous archives of yield data secured from that area. Data mining is one such procedure practised to predict the crop yield. The systems examine the data, and on mining, several patterns based on numerous parameters predict the return. This article directs on crop yield forecast in Trichy district by adopting data mining techniques for rule formation on classifying the training data and implementing prediction for test data. The suggested method employs fuzzy C means algorithm for clustering and multilayer perceptron design for prediction. The results of accuracy and execution time of the proposed system correlated with the regression algorithm of prediction.

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Performance of detrending models of crop yield risk assessment: evaluation on real and hypothetical yield data

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-014-0871-x ◽

2014 ◽

Vol 29 (1) ◽

pp. 109-117 ◽

Cited By ~ 17

Author(s):

Tao Ye ◽

Jianliang Nie ◽

Jun Wang ◽

Peijun Shi ◽

Zhu Wang

Keyword(s):

Risk Assessment ◽

Crop Yield ◽

Yield Data ◽

Yield Risk

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A multi-model analysis of teleconnected crop yield variability in a range of cropping systems

10.5194/esd-2019-8 ◽

2019 ◽

Cited By ~ 1

Author(s):

Matias Heino ◽

Joseph H. A. Guillaume ◽

Christoph Müller ◽

Toshichika Iizumi ◽

Matti Kummu

Keyword(s):

Crop Yield ◽

Crop Production ◽

Food System ◽

Crop Yields ◽

Southern Oscillation ◽

Yield Variability ◽

Management Scenarios ◽

Yield Data ◽

Climate Oscillations ◽

Crop Models

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño Southern Oscillation (ENSO), which has been found to impact crop yields in all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to impact crop production especially in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD and NAO on the growing conditions of maize, rice, soybean and wheat at the global scale, by utilizing crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that simulated crop yield variability is correlated to climate oscillations to a wide extent (up to almost half of all maize and wheat harvested areas for ENSO) and in several important crop producing areas, e.g. in North America (ENSO, wheat), Australia (IOD & ENSO, wheat) and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed, and fully fertilized scenarios, while the sensitivity tends to be lower if crops are fully irrigated. Since, the development of ENSO, IOD and NAO can be reliably forecasted in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate related shocks.

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Linking Cover Crop Residue Quality and Tillage System to CO2-C Emission, Soil C and N Stocks and Crop Yield Based on a Long-Term Experiment

Agronomy ◽

10.3390/agronomy10121848 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1848

Author(s):

Otávio A. Leal ◽

Telmo J. C. Amado ◽

Jackson E. Fiorin ◽

Cristiano Keller ◽

Geovane B. Reimche ◽

...

Keyword(s):

Crop Yield ◽

Cover Crops ◽

No Tillage ◽

Yield Data ◽

Soil C ◽

Term Experiment ◽

Long Term Experiment ◽

Soil C And N ◽

C And N

Cover crops (CC), particularly legumes, are key to promote soil carbon (C) sequestration in no-tillage. Nevertheless, the mechanisms regulating this process need further elucidation within a broad comprehensive framework. Therefore, we investigated effects of CC quality: black oat (Avena strigosa Schreb) (oat), common vetch (Vicia sativa L.) (vetch), and oat + vetch on carbon dioxide-C (CO2-C) emission (124 days) under conventional- (CT), minimum- (MT) and no-tillage (NT) plots from a long-term experiment in Southern Brazil. Half-life time (t1/2) of CC residues and the apparent C balance (ACB) were obtained for CT and NT. We linked our data to long-term (22 years) soil C and nitrogen (N) stocks and crop yield data of our experimental field. Compared to CT, NT increased t1/2 of oat, oat + vetch and vetch by 3.9-, 3.1- and 3-fold, respectively; reduced CO2-C emissions in oat, oat + vetch and vetch by 500, 600 and 642 kg ha−1, respectively; and increased the ACB (influx) in oat + vetch (195%) and vetch (207%). For vetch, CO2-C emission in MT was 77% greater than NT. Legume CC should be preferentially combined with NT to reduce CO2-C emissions and avoid a flush of N into the soil. The legume based-NT system showed the greatest soil C and N sequestration rates, which were significantly and positively related to soybean (Glycine max (L.) Merrill) and maize (Zea mays L.) yield. Soil C (0–90 cm depth) and N (0–100 cm depth) sequestration increments of 1 kg ha−1 corresponded to soybean yield increments of 1.2 and 7.4 kg ha−1, respectively.

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