Quantifying individual and collective influences of soil properties on crop yield

Quantification of the agronomic influences of soil properties, collected at high sampling resolution, on crop yield is essential for site specific soil management. The objective of this study was to implement a novel Volterra Non-linear Regressive with eXogenous inputs (VNRX) model accounting for the linear and non-linear variability (VNRX-LN) to quantify causal factors affecting wheat yield in a 22-ha field with a waterlogging problem in Bedfordshire, UK. The VNRX-LN model was applied using high-resolution data of eight key soil properties (total nitrogen (TN), organic carbon, pH, available phosphorous, magnesium (Mg), calcium, moisture content and cation exchange capacity (CEC)). The data were collected with an on-line (tractor mounted) visible and near infrared spectroscopy sensor and used as multiple-input to the VNRX-LN model, whereas crop yield represented the single-output in the system. Results showed that the largest contributors to wheat yield were CEC, Mg and TN, with error reduction ratio contribution values of 14.6%, 4.69% and 1% respectively. The overall contribution of the soil properties considered in this study equalled 23.21%. This was attributed to a large area of the studied field having been waterlogged, which masked the actual effect of soil properties on crop yield. It is recommended that VNRX-LN is validated on a larger number of fields, where other crop yield affecting parameters e.g., crop disease, pests, drainage, topography and microclimate conditions should be taken into account.

Download Full-text

Growth profile based crop yield models: A case study of large area wheat yield modelling and its extendibility using atmospheric corrected NOAA AVHRR data

International Journal of Remote Sensing ◽

10.1080/01431160210156018 ◽

2003 ◽

Vol 24 (10) ◽

pp. 2037-2054 ◽

Cited By ~ 19

Author(s):

M. H. Kalubarme ◽

M. B. Potdar ◽

K. R. Manjunath ◽

R. K. Mahey ◽

S. S. Siddhu

Keyword(s):

Crop Yield ◽

Wheat Yield ◽

Large Area ◽

Growth Profile ◽

Noaa Avhrr ◽

Avhrr Data

Download Full-text

Analysis of the spatial variability of crop yield and soil properties in small agricultural plots

Bragantia ◽

10.1590/s0006-87052003000100016 ◽

2003 ◽

Vol 62 (1) ◽

pp. 127-138 ◽

Cited By ~ 45

Author(s):

Sidney Rosa Vieira ◽

Antonio Paz Gonzalez

Keyword(s):

Spatial Variability ◽

Soil Properties ◽

Crop Yield ◽

Kriging Interpolation ◽

Climate Conditions ◽

Factors Affecting ◽

Semivariogram Analysis ◽

Map Generation ◽

Crop Cycle ◽

One Year

The objective of this study was to assess spatial variability of soil properties and crop yield under no tillage as a function of time, in two soil/climate conditions in São Paulo State, Brazil. The two sites measured approximately one hectare each and were cultivated with crop sequences which included corn, soybean, cotton, oats, black oats, wheat, rye, rice and green manure. Soil fertility, soil physical properties and crop yield were measured in a 10-m grid. The soils were a Dusky Red Latossol (Oxisol) and a Red Yellow Latossol (Ultisol). Soil sampling was performed in each field every two years after harvesting of the summer crop. Crop yield was measured at the end of each crop cycle, in 2 x 2.5 m sub plots. Data were analysed using semivariogram analysis and kriging interpolation for contour map generation. Yield maps were constructed in order to visually compare the variability of yields, the variability of the yield components and related soil properties. The results show that the factors affecting the variability of crop yield varies from one crop to another. The changes in yield from one year to another suggest that the causes of variability may change with time. The changes with time for the cross semivariogram between phosphorus in leaves and soybean yield is another evidence of this result.

Download Full-text

Modelling the Influence of Soil Properties on Crop Yields Using a Non-Linear NFIR Model and Laboratory Data

Soil Systems ◽

10.3390/soilsystems5010012 ◽

2021 ◽

Vol 5 (1) ◽

pp. 12

Author(s):

Rebecca L. Whetton ◽

Yifan Zhao ◽

Said Nawar ◽

Abdul M. Mouazen

Keyword(s):

Soil Properties ◽

Crop Yield ◽

Crop Yields ◽

Finite Impulse Response ◽

Laboratory Data ◽

Weather Conditions ◽

Total Contribution ◽

Non Linear ◽

Correlation Analysis Method ◽

Cropping Seasons

This paper introduces a new non-linear correlation analysis method based on a non-linear finite impulse response (NFIR) model to study and quantify the effects of ten soil properties on crop yield. Two versions of the NFIR model were implemented: NFIR-LN, accounting for both the linear and non-linear variability in the system, and NFIR-L, accounting for linear variability only. The performance of the NFIR models was compared with a non-linear random forest (RF) model, to predict oilseed rape (2013) and wheat (2014) yields in one field at Premslin, Germany. The ten soil properties were used as system inputs, whereas crop yield was the system output. Results demonstrated that the individual and total contribution of the soil properties on crop yield varied throughout the different cropping seasons, weather conditions, and crops. Both the NFIR-LN and RF models outperformed the NFIR-L model and explained up to 55.62% and 50.66% of the yield variation for years 2013 and 2014, respectively. The NFIR-LN and RF models performed equally during yield prediction, although the NFIR-LN model provided more consistent results through the two cropping seasons. Higher phosphorus and potassium contributions to the yield were calculated with the NFIR-LN model, suggesting this method outperforms the RF model.

Download Full-text

Biochar combined with nitrogen fertilizer affects soil properties and wheat yield in medium‐low‐yield farmland

Soil Use and Management ◽

10.1111/sum.12712 ◽

2021 ◽

Author(s):

Linlin Dong ◽

Jidong Wang ◽

Mingxing Shen ◽

Haidong Zhang ◽

Lingqing Wang ◽

...

Keyword(s):

Soil Properties ◽

Nitrogen Fertilizer ◽

Wheat Yield

Download Full-text

One-Pot Hydrothermal Synthesis of Victoria Green (Ca3Cr2Si3O12) Nanoparticles in Alkaline Fluids and Its Colour Hue Characterisation

Nanomaterials ◽

10.3390/nano11020521 ◽

2021 ◽

Vol 11 (2) ◽

pp. 521

Author(s):

Juan Carlos Rendón-Angeles ◽

Zully Matamoros-Veloza ◽

Jose Luis Rodríguez-Galicia ◽

Gimyeong Seong ◽

Kazumichi Yanagisawa ◽

...

Keyword(s):

Near Infrared ◽

Single Step ◽

Spectral Space ◽

Infrared Reflectance ◽

Hydrothermal Preparation ◽

Near Infrared Reflectance ◽

One Pot ◽

Factors Affecting ◽

B Values ◽

Alkaline Conditions

One-pot hydrothermal preparation of Ca3Cr2Si3O12 uvarovite nanoparticles under alkaline conditions was investigated for the first time. The experimental parameters selected for the study considered the concentration of the KOH solvent solution (0.01 to 5.0 M), the agitation of the autoclave (50 rpm), and the nominal content of Si4+ (2.2–3.0 mole). Fine uvarovite particles were synthesised at 200 °C after a 3 h interval in a highly concentrated 5.0 M KOH solution. The crystallisation of single-phase Ca3Cr2Si3O12 particles proceeded free of by-products via a one-pot process involving a single-step reaction. KOH solutions below 2.5 M and water hindered the crystallisation of the Ca3Cr2Si3O12 particles. The hydrothermal treatments carried out with stirring (50 rpm) and non-stirring triggered the crystallisation of irregular anhedral particles with average sizes of 8.05 and 12.25 nm, respectively. These particles spontaneously assembled into popcorn-shaped agglomerates with sizes varying from 66 to 156 nm. All the powders prepared by the present method exhibited CIE-L*a*b* values that correspond to the Victoria green colour spectral space and have a high near infrared reflectance property. The particle size and structural crystallinity are factors affecting the Victoria pigment optical properties, such as CIE-L*a*b* values, green tonality, and near-infrared reflectance.

Download Full-text

Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

Plant and Soil ◽

10.1007/s11104-020-04775-y ◽

2020 ◽

Author(s):

L. Granlund ◽

M. Keinänen ◽

T. Tahvanainen

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

High Resolution Imaging ◽

Factors Affecting ◽

Vector Machines ◽

Resolution Imaging ◽

Shortwave Infrared ◽

Type Classification ◽

Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

Download Full-text