scholarly journals Relationship between Spatial Variability Pattern of Wheat Yield and Soil Properties

2018 ◽  
pp. 1-14
Author(s):  
Alidad Karami ◽  
Sadegh Afzalinia
Keyword(s):  
Spatial Structure ◽  
Soil Properties ◽  
Soil Ph ◽  
Precision Agriculture ◽  
Interpolation Method ◽  
Irrigation System ◽  
Wheat Yield ◽  
Gaussian Model ◽  
Variogram Model ◽  
Clay Percentage

Aims: Determining effects of spatial variation of some soil properties on wheat quantity and quality variation in order that proper soil and inputs management can be applied for sustainable wheat production. Study Design: Analyzing data of a field with center pivot irrigation system and uniform management using the geostatistical method. Place and Duration of Study: Soil and Water Research Department, Fars Agricultural and Natural Resources Research and Education Center, Darab, Iran, from September 2013 to February 2014. Methodology: Wheat yield data harvested by class lexion 510 combine from 25 m2 plots (11340 locations) with the corresponding geographical location were used. Besides, soil properties and wheat yield were measured at 36 randomly selected points on the field. Interpolation of parameters was predicted with the best semi-variogram model using kriging, inverse distance weighted (IDW), and cokriging methods. Results: Results showed that wheat yield varied from 2 to 10.08 tons per hectare. Cokriging with cofactor of kernel weight interpolator had more accuracy compared to the combine default interpolator (kriging). A logical, linear correlation was found between different parameters. The best variogram model for pH, OC, and ρb was exponential, for EC, TNV, SP, soil silt and clay percentage was spherical, and for soil, percentage sand was Gaussian model. Data of soil sand, silt, and clay percentage, EC, TNV, and SP had strong spatial structure, and soil pH, OC, and ρb had moderate spatial structure. The best interpolation method for soil pH, EC, sand and silt percentage was kriging method; while, for TNV, SP, OC, ρb, and clay percentage was IDW. Conclusion: There was a close relationship between wheat yield variation and changes in the soil properties. Soil properties and wheat yield distribution maps provided valuable information which could be used for wheat yield improvement in precision agriculture.

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

2021 ◽  
Author(s):  
Linlin Dong ◽  
Jidong Wang ◽  
Mingxing Shen ◽  
Haidong Zhang ◽  
Lingqing Wang ◽  
...  
Keyword(s):  
Soil Properties ◽  
Nitrogen Fertilizer ◽  
Wheat Yield

scholarly journals Soil Nutrient and Vegetation Diversity Patterns of Alpine Wetlands on the Qinghai-Tibetan Plateau

2021 ◽  
Vol 13 (11) ◽  
pp. 6221
Author(s):  
Muyuan Ma ◽  
Yaojun Zhu ◽  
Yuanyun Wei ◽  
Nana Zhao
Keyword(s):  
Species Diversity ◽  
Tibetan Plateau ◽  
Soil Properties ◽  
Soil Ph ◽  
Plant Biomass ◽  
Principal Component ◽  
Vegetation Diversity ◽  
Vegetation Biomass ◽  
Relative Contribution ◽  
The Relationship

To predict the consequences of environmental change on the biodiversity of alpine wetlands, it is necessary to understand the relationship between soil properties and vegetation biodiversity. In this study, we investigated spatial patterns of aboveground vegetation biomass, cover, species diversity, and their relationships with soil properties in the alpine wetlands of the Gannan Tibetan Autonomous Prefecture of on the Qinghai-Tibetan Plateau, China. Furthermore, the relative contribution of soil properties to vegetation biomass, cover, and species diversity were compared using principal component analysis and multiple regression analysis. Generally, the relationship between plant biomass, coverage, diversity, and soil nutrients was linear or unimodal. Soil pH, bulk density and organic carbon were also significantly correlated to plant diversity. The soil attributes differed in their relative contribution to changes in plant productivity and diversity. pH had the highest contribution to vegetation biomass and species richness, while total nitrogen was the highest contributor to vegetation cover and nitrogen–phosphorus ratio (N:P) was the highest contributor to diversity. Both vegetation productivity and diversity were closely related to soil properties, and soil pH and the N:P ratio play particularly important roles in wetland vegetation biomass, cover, and diversity.

Seasonal changes in multi-scale spatial structure of soil pH and related parameters along a tropical dry evergreen forest slope

Geoderma ◽  
2011 ◽  
Vol 165 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Naoyuki Yamashita ◽  
Seiichi Ohta ◽  
Hiroyuki Sase ◽  
Bopit Kievuttinon ◽  
Jesada Luangjame ◽  
...  
Keyword(s):  
Spatial Structure ◽  
Soil Ph ◽  
Seasonal Changes ◽  
Evergreen Forest ◽  
Multi Scale ◽  
Dry Evergreen Forest ◽  
Tropical Dry Evergreen Forest

scholarly journals Spatial variability and mapping of soil fertility status in a high-potential smallholder farming area under sub-humid conditions in Zimbabwe

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gabriel Soropa ◽  
Olton M. Mbisva ◽  
Justice Nyamangara ◽  
Ermson Z. Nyakatawa ◽  
Newton Nyapwere ◽  
...  
Keyword(s):  
Spatial Variability ◽  
Soil Properties ◽  
Soil Ph ◽  
Strong Evidence ◽  
Spatial Dependence ◽  
Random Component ◽  
Smallholder Farming ◽  
Mineral N ◽  
Spatial Trend ◽  
Spatial Trends

AbstractA study was conducted to examine spatial variability of soil properties related to fertility in maize fields across varying soil types in ward 10 of Hurungwe district, Zimbabwe; a smallholder farming area with sub-humid conditions and high yield potential. Purposively collected and geo-referenced soil samples were analyzed for texture, pH, soil organic carbon (OC), mineral N, bicarbonate P, and exchangeable K. Linear mixed model was used to analyze spatial variation of the data. The model allowed prediction of soil properties at unsampled sites by the empirical best linear unbiased predictor (EBLUP). Evidence for spatial dependence in the random component of the model was evaluated by calculating Akaike’s information criterion. Soil pH ranged from 4.0 to 6.9 and showed a strong spatial trend increasing from north to south, strong evidence for a difference between the home and outfields with homefields significantly higher and between soil textural classes with the sand clay loam fraction generally higher. Soil OC ranged from 0.2 to 2.02% and showed no spatial trend, but there was strong evidence for a difference between home and outfields, with mean soil OC in homefields significantly larger, and between soil textural classes, with soil OC largest in the sandy clay loams. Both soil pH and OC showed evidence for spatial dependence in the random effect, providing a basis for spatial prediction by the EBLUP, which was presented as a map. There were significant spatial trends in mineral N, available P and exchangeable K, all increasing from north to south; significant differences between homefields and outfields (larger concentrations in homefields), and differences between the soil textural classes with larger concentrations in the sandy clay loams. However, there was no evidence for spatial dependence in the random component, so no attempt was made to map these variables. These results show how management (home fields vs outfields), basic soil properties (texture) and other factors emerging as spatial trends influence key soil properties that determine soil fertility in these conditions. This implies that the best management practices may vary spatially, and that site-specific management is a desirable goal in conditions such as those which apply in Ward 10 of Hurungwe district in Zimbabwe.

EFFECTS OF LIME ON EXTRACTABLE ALUMINUM AND OTHER SOIL PROPERTIES AND ON BARLEY AND ALFALFA GROWN IN POT TESTS

1972 ◽  
Vol 52 (3) ◽  
pp. 427-438 ◽  
Author(s):  
A. J. MacLEAN ◽  
R. L. HALSTEAD ◽  
B. J. FINN
Keyword(s):  
Plant Growth ◽  
Soil Properties ◽  
Soil Ph ◽  
Acid Soil ◽  
Soil Samples ◽  
Neutral Salt ◽  
Incubation Experiment ◽  
Pot Experiments ◽  
P Content ◽  
Mn Content

Liming of six acid soil samples in an incubation experiment with rates to raise the soil pH to 6.0 or above eliminated Al soluble in 0.01 M CaCl2, reduced soluble Mn and Zn, increased NO3-N markedly, and at the highest pH increased the amounts of NaHCO3-soluble P in some of the soils. In corresponding pot experiments, liming increased the yield of alfalfa and in three of the soils the yield of barley also. Liming reduced the concentrations of the metals in the plants and at the highest pH tended to increase the P content of the plants. Liming to a pH of about 5.3 eliminated or greatly reduced soluble Al and the soils were base saturated as measured by the replacement of Al, Ca, and Mg by a neutral salt. There was some evidence that liming to reduce soluble Al and possibly Mn was beneficial for plant growth. Gypsum increased the concentrations of Al, Mn, and Zn in 0.01 M CaCl2 extracts of the soils whereas phosphate reduced them. The changes in the Mn content of the plants following these treatments were in agreement with the amounts of Mn in the CaCl2 extracts.

The interaction between soil pH and phosphorus for wheat yield and the impact of lime-induced changes to soil aluminium and potassium

Soil Research ◽  
2017 ◽  
Vol 55 (4) ◽  
pp. 341 ◽  
Author(s):  
Craig A. Scanlan ◽  
Ross F. Brennan ◽  
Mario F. D'Antuono ◽  
Gavin A. Sarre
Keyword(s):  
Grain Yield ◽  
Soil Ph ◽  
Field Experiments ◽  
Wheat Yield ◽  
Acid Soils ◽  
Combined Effect ◽  
Additive Effect ◽  
Yield Response ◽  
Response Curves ◽  
The Impact

Interactions between soil pH and phosphorus (P) for plant growth have been widely reported; however, most studies have been based on pasture species, and the agronomic importance of this interaction for acid-tolerant wheat in soils with near-sufficient levels of fertility is unclear. We conducted field experiments with wheat at two sites with acid soils where lime treatments that had been applied in the 6 years preceding the experiments caused significant changes to soil pH, extractable aluminium (Al), soil nutrients and exchangeable cations. Soil pH(CaCl2) at 0–10cm was 4.7 without lime and 6.2 with lime at Merredin, and 4.7 without lime and 6.5 with lime at Wongan Hills. A significant lime×P interaction (P<0.05) for grain yield was observed at both sites. At Merredin, this interaction was negative, i.e. the combined effect of soil pH and P was less than their additive effect; the difference between the dose–response curves without lime and with lime was greatest at 0kgPha–1 and the curves converged at 32kgPha–1. At Wongan Hills, the interaction was positive (combined effect greater than the additive effect), and lime application reduced grain yield. The lime×P interactions observed are agronomically important because different fertiliser P levels were required to maximise grain yield. A lime-induced reduction in Al phytotoxicity was the dominant mechanism for this interaction at Merredin. The negative grain yield response to lime at Wongan Hills was attributed to a combination of marginal soil potassium (K) supply and lime-induced reduction in soil K availability.

scholarly journals Improvement of Tea (Camellia sinensis L.) Soil Properties by Growing Different Green Crops

2015 ◽  
Vol 12 (2) ◽  
pp. 34-38 ◽  
Author(s):  
Ashim Kumar Saha ◽  
Apu Biswas ◽  
Abdul Qayyum Khan ◽  
Md. Mohashin Farazi ◽  
Md. Habibur Rahman
Keyword(s):  
Organic Carbon ◽  
Soil Properties ◽  
Total Nitrogen ◽  
Soil Ph ◽  
Nutritional Value ◽  
Soil Health ◽  
Critical Value ◽  
Available Phosphorus ◽  
Organic Carbon Content

Long-term tea cultivation has led to degradation of the soil. Old tea soils require rehabilitation for restoring soil health. Soil rehabilitation by growing different green crops can break the chain of monoculture of tea. An experiment was conducted at The Bangladesh Tea Research Institute (BTRI) Farm during 2008-2011 to find out the efficiency of different green crops on the improvement of soil properties. Four green crops such as Guatemala, Citronella, Mimosa and Calopogonium were grown to develop the nutritional value of the degraded tea soil. Soil samples were collected and analyzed before and at the end of experiment. Soil pH was increased in all four green crops treated plots with the highest increase in Citronella treated plots (from 4.1 to 4.5). Highest content of organic carbon (1.19%) and total nitrogen (0.119%) were found in Mimosa and Calopogonium treated plots, respectively. Concentration of available phosphorus, calcium and magnesium in all green crops treated plots were above the critical values, while available potassium content was above the critical value in Guatemala, Citronella and Mimosa treated plots. Changes in soil pH and available potassium were significant, while changes in organic carbon content, total nitrogen and available calcium were insignificant. Changes in available phosphorus and magnesium were significant. The Agriculturists 2014; 12(2) 34-38

scholarly journals Evolutionary and ecological perspectives on the wheat phenotype

2021 ◽  
Vol 288 (1958) ◽  
pp. 20211259
Author(s):  
Victor O. Sadras
Keyword(s):  
Precision Agriculture ◽  
Current Model ◽  
Crop Improvement ◽  
Wheat Yield ◽  
High Yield ◽  
Striking Similarity ◽  
Stabilizing Selection ◽  
Grain Number ◽  
Offspring Number ◽  
Trade Offs

Technologies, from molecular genetics to precision agriculture, are outpacing theory, which is becoming a bottleneck for crop improvement. Here, we outline theoretical insights on the wheat phenotype from the perspective of three evolutionary and ecologically important relations—mother–offspring, plant–insect and plant–plant. The correlation between yield and grain number has been misinterpreted as cause-and-effect; an evolutionary perspective shows a striking similarity between crop and fishes. Both respond to environmental variation through offspring number; seed and egg size are conserved. The offspring of annual plants and semelparous fishes, lacking parental care, are subject to mother–offspring conflict and stabilizing selection. Labile reserve carbohydrates do not fit the current model of wheat yield; they can stabilize grain size, but involve trade-offs with root growth and grain number, and are at best neutral for yield. Shifting the focus from the carbon balance to an ecological role, we suggest that labile carbohydrates may disrupt aphid osmoregulation, and thus contribute to wheat agronomic adaptation. The tight association between high yield and low competitive ability justifies the view of crop yield as a population attribute whereby the behaviour of the plant becomes subordinated within that of the population, with implications for genotyping, phenotyping and plant breeding.

scholarly journals Geostatistical prediction of clay percentage based on soil survey data

2002 ◽  
Vol 11 (4) ◽  
pp. 381-390
Author(s):  
A. TALKKARI ◽  
L. JAUHIAINEN ◽  
M. YLI-HALLA
Keyword(s):  
Soil Properties ◽  
Spatial Variation ◽  
Survey Data ◽  
Clay Content ◽  
Spherical Model ◽  
Simulation Method ◽  
Soil Survey ◽  
Management Zones ◽  
Additional Information ◽  
Clay Percentage

In precision farming fields may be divided into management zones according to the spatial variation in soil properties. Clay content is an important soil characteristic, because it is associated with other soil properties that are important in management. Soil survey data from 150 sampling sites taken from an area of 218 ha were used to predict the spatial variation of clay percentage geostatistically in an agricultural soil in Jokioinen, Finland. The exponential and spherical models with a nugget component were fitted to the experimental variogram. This indicated that the medium-range pattern could be modelled, but the short-range variation could not, due to sparsity of sample points at short distances. The effect of sampling density on the kriging error was evaluated using the random simulation method. Kriging with a spherical model produced a map with smooth variation in clay percentage. The standard error of kriging estimates decreased only slightly when the density of samples was increased. The predictions were divided into three classes based on the clay percentage. Areas with clay content below 30%, between 30% and 60% and over 60% belong to non-clay, clay and heavy clay zones, respectively. With additional information from the soil samples on the contents of nutrients and organic matter these areas can serve as agricultural management zones.;

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