APS consists of household- and plot-level data on 336 households in maize-growing areas of Jimma and Bako zones. This dataset consists of information about Meher production and inputs in 2016. dataset variables observations characterstics_all_plots_2016 18 2504 production_and_family_labor_meher 47 2078 input_cost_meher 9 3024 hired_services_meher 15 1286 There are two other linked data sets on Belg production and the Focal Plot Note that dates are in the Ethiopian calendar. see, https://en.wikipedia.org/wiki/Ethiopian_calendar"
Soil analysis from 0-20 cm and 20-50 cm depths in 140 farmer fields from the in Southern Highlands, Eastern and Northern Zones of Tanzania in 2014/2015.
The dataset was obtained from farmers' fields covered during UAV flight mission at five locations (across 5 LGAs) within the TAMASA's focal states. The number of volunteer farmers are uneven across the LGAs. Five(5) quadrats of 2m X 2m were created within each farmers field for data collection. Data on NDVI, canopy cover and plant height was collected at 4WAS and 8WAS by Greenseeker, and NDVI at 4 and 8 WAS by UAV within the farmers field. At maturity grain yield was measured.
This data covers the crop aspect of the Agronomy Panel Survey (APS) implemented in 99 selected communities across 17 LGAs in 22 randomly selected 10km x 10 km sampling grids from Kano, Kaduna and Katsina States. The XLS file has tabs for Metadata, Variables, Data and Data history.
In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, while researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although statistical models are usually mathematically elegant, they are also computatio nally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: a) item-based collaborative filtering (IBCF; method M1) and b) the matrix factorization algorithm (method M2) in the context of multiple traits and multiple environments. The IBCF and matrix factorization methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique (method M1) was slightly better in terms of prediction accuracy than the two conventional methods and the matrix factorization method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.
Use of remote sensing based radar images for zero tillage detection in Sinaloa (Municipality of Santiago, El Fuerte and Guasave) and Sonora (Municipality of Angostura, Mocorito, Navolato, Culiacan, San Ignacio Rio Muerto, Cajeme, Benito Juarez, Navojoa, Etchojoa), Mexico.
Use of remote sensing based radar images for zero tillage detection in Sinaloa (Municipality of Santiago, El Fuerte and Guasave) and Sonora (Municipality of Angostura, Mocorito, Navolato, Culiacan, San Ignacio Rio Muerto, Cajeme, Benito Juarez, Navojoa, Etchojoa), Mexico.
