Mapping and Managing Spatial Patterns In Soil Fertility and Crop Yield

2015 ◽  
pp. 15-26 ◽  
Author(s):  
D.J. Mulla
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Spatial Patterns

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

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.

scholarly journals Crop Yield and Soil Fertility Status of Long-Term Rice-Rice-Wheat Cropping Systems

2017 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Nabin Rawal ◽  
Rajan Ghimire ◽  
Devraj Chalise
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Management Practices ◽  
Crop Yields ◽  
Oryza Sativa L ◽  
Farmyard Manure ◽  
Wheat Yield ◽  
Nutrient Supply ◽  
Available Phosphorus

Balanced nutrient supply is important for the sustainable crop production. We evaluated the effects of nutrient management practices on soil properties and crop yields in rice (Oryza sativa L.) - rice - wheat (Triticum aestivum L.) system in a long-term experiment established at National Wheat Research Program (NWRP), Bhairahawa, Nepal. The experiment was designed as a randomized complete block experiment with nine treatments and three replications. Treatments were applied as: T1- no nutrients added, T2- N added; T3- N and P added; T4- N and K added; T5- NPK added at recommended rate for all crops. Similarly, T6- only N added in rice and NPK in wheat at recommended rate; T7- half N; T8- half NP of recommended rate for both crops; and T9- farmyard manure (FYM) @10 Mg ha-1 for all crops in rotation. Results of the study revealed that rice and wheat yields were significantly greater under FYM than all other treatments. Treatments that did not receive P (T2, T3, T7, T8) and K (T2, T4) had considerably low wheat yield than treatments that received NPK (T5) and FYM (T9). The FYM lowered soil pH and improved soil organic matter (SOM), total nitrogen (TN), available phosphorus (P), and exchangeable potassium (K) contents than other treatments. Management practices that ensure nutrient supply can increase crop yield and improve soil fertility status.Int. J. Appl. Sci. Biotechnol. Vol 5(1): 42-50

Analysing and simulating spatial patterns of crop yield in Guizhou Province based on artificial neural networks

2020 ◽  
pp. 030913332095663 ◽  
Author(s):  
Boyi Liang ◽  
Hongyan Liu ◽  
Timothy A Quine ◽  
Xiaoqiu Chen ◽  
Paul D Hallett ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Spatial Patterns ◽  
Crop Production ◽  
Guizhou Province ◽  
Crop Species ◽  
Environmental Controls ◽  
Artificial Neural ◽  
Propagation Network

The area of karst terrain in China covers 3.63×106 km2, with more than 40% in the southwestern region over the Guizhou Plateau. Karst comprises exposed carbonate bedrock over approximately 1.30×106 km2 of this area, which suffers from soil degradation and poor crop yield. This paper aims to gain a better understanding of the environmental controls on crop yield in order to enable more sustainable use of natural resources for food production and development. More precisely, four kinds of artificial neural network were used to analyse and simulate the spatial patterns of crop yield for seven crop species grown in Guizhou Province, exploring the relationships with meteorological, soil, irrigation and fertilization factors. The results of spatial classification showed that most regions of high-level crop yield per area and total crop yield are located in the central-north area of Guizhou. Moreover, the three artificial neural networks used to simulate the spatial patterns of crop yield all demonstrated a good correlation coefficient between simulated and true yield. However, the Back Propagation network had the best performance based on both accuracy and runtime. Among the 13 influencing factors investigated, temperature (16.4%), radiation (15.3%), soil moisture (13.5%), fertilization of N (13.5%) and P (12.4%) had the largest contribution to crop yield spatial distribution. These results suggest that neural networks have potential application in identifying environmental controls on crop yield and in modelling spatial patterns of crop yield, which could enable local stakeholders to realize sustainable development and crop production goals.

Improving Soil Fertility and Crop Yield in a Tropical Region with Palisadegrass Cover Crops

2015 ◽  
Vol 107 (6) ◽  
pp. 2271-2280 ◽  
Author(s):  
Carlos A.C. Crusciol ◽  
Adriano S. Nascente ◽  
Emerson Borghi ◽  
Rogério P. Soratto ◽  
Priscila O. Martins
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Cover Crops ◽  
Tropical Region

scholarly journals Spatial variability of soil fertility and its relation with cocoa yield

2017 ◽  
Vol 21 (2) ◽  
pp. 88-93 ◽  
Author(s):  
Railton O. dos Santos ◽  
◽  
Laís B. Franco ◽  
Samuel A. Silva ◽  
George A. Sodré ◽  
...  
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Spatial Variability ◽  
Soil Fertility ◽  
Soil Properties ◽  
Crop Yield ◽  
Chemical Properties ◽  
Western Region ◽  
Chemical Attributes ◽  
Cocoa Yield

ABSTRACT The knowledge on the spatial variability of soil properties and crops is important for decision-making on agricultural management. The objective of this study was to evaluate the spatial variability of soil fertility and its relation with cocoa yield. The study was conducted over 14 months in an area cultivated with cocoa. A sampling grid was created to study soil chemical properties and cocoa yield (stratified in season, off-season and annual). The data were analyzed using descriptive and exploratory statistics, and geostatistics. The chemical attributes were classified using fuzzy logic to generate a soil fertility map, which was correlated with maps of crop yield. The soil of the area, except for the western region, showed possibilities ranging from medium to high for cocoa cultivation. Soil fertility showed positive spatial correlation with cocoa yield, and its effect was predominant only for the off-season and annual cocoa.

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