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Besigye, D

Scott, B

Amin, M.E

Graff, N

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Authors

Aduramigba-Modupe, V

Amapu, I

Walsh, M

Scott, B

Amin, M.E

Abdelfattah, M.A

Mohamed, E.S

Belal, A.A

Nabil, M

Mahmoud, A.G

Opolot, E

Besigye, D

Musinguzi, P

Alou, I.N

Basamba, T.A

Olupot, G

Ludemann, C

Dobermann, A

Graff, N

Sela, S

van Loon, M

Hijbeek, R

van Ittersum, M

Topics

Precision Nutrient Management

Precision Planting/Harvesting

Precision Water Management

Precision Nutrient Management

Type

Oral

Poster

Year

2020

2022

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Authors

Filter results4 paper(s) found.
1. Methodology for Assessing Nutrient Status of Nigeria Croplands: AfSIS/NiSIS Pilot Project - Pathway for Precision Agriculture Mapping Inherently low soil fertility, nutrient imbalances and accelerating degradation constitute threats to precision agriculture (PA), agricultural productivity and ecosystem services in sub-Saharan Africa (Nigeria inclusive). Presently, the geographical extent of existing nutrient constraints, location specific trends and opportunities for managing these over time are highly uncertain. The AfSIS/NiSIS project assessment aims to provide spatially explicit observations, measurements and predictions... V. Aduramigba-modupe, I. Amapu, M. Walsh, B. Scott
2. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud
3. Soil Moisture Sensing for Increased Water Use Efficiency and Crop Yields in Furrow Irrigated Systems: a Case of Mubuku Irrigation Scheme in Uganda Water and nutrient use efficiencies remain low in most of the irrigated systems in developing countries, undermining crop productivity in these areas. This is partly due to lack of precise and site-specific information to guide irrigation scheduling. In most cases information on soil properties and crop water requirements is lacking and thus irrigation scheduling is done on rotational basis whether the crop needs water or not. Consequently, the amount of water stored in soil root zone for crop... E. Opolot, D. Besigye, P. Musinguzi, I.N. Alou, T.A. Basamba, G. Olupot
4. The Global Crop Nutrient Removal Database (Gcnrd): Development, Initial Analysis and Identification of Current Data Gaps Improved understanding of the factors affecting nutrient uptake can enable production optimization, increased nutrient use efficiencies, reduced environmental footprints, and overall higher economic returns for farmers. Crop uptake is affected by genetics, soil, weather, agronomic practices, and their interactions. As such, understanding the way crop nutrient uptake varies across different regions and production environments requires the coverage of various parameters, and necessitates collaboration... C. Ludemann, A. Dobermann, N. Graff, S. Sela, M. Van loon, R. Hijbeek, M. Van ittersum

Filter results4 paper(s) found.

1. Methodology for Assessing Nutrient Status of Nigeria Croplands: AfSIS/NiSIS Pilot Project - Pathway for Precision Agriculture Mapping

Inherently low soil fertility, nutrient imbalances and accelerating degradation constitute threats to precision agriculture (PA), agricultural productivity and ecosystem services in sub-Saharan Africa (Nigeria inclusive). Presently, the geographical extent of existing nutrient constraints, location specific trends and opportunities for managing these over time are highly uncertain. The AfSIS/NiSIS project assessment aims to provide spatially explicit observations, measurements and predictions... V. Aduramigba-modupe, I. Amapu, M. Walsh, B. Scott

2. Potato Yield Prediction Using Multi-temporal Sentinel-2 Data and Multiple Linear Regression

Traditional potato growth models have a number of flaws, i.e., the cost of data collection, quality of input data, and the absence of spatial information in some cases. To address these challenges, we created a multiple linear regression model (MLRM) that uses the multi-temporal Sentinel-2 derived indices to predict potato yield. Along the growing season (from October 2019 to February 2020) eight Sentinel-2 imageries were collected, afterwards, the normalized difference vegetation index (NDVI)... M.E. Amin, M.A. Abdelfattah, E.S. Mohamed, A.A. Belal, M. Nabil, A.G. Mahmoud

3. Soil Moisture Sensing for Increased Water Use Efficiency and Crop Yields in Furrow Irrigated Systems: a Case of Mubuku Irrigation Scheme in Uganda

Water and nutrient use efficiencies remain low in most of the irrigated systems in developing countries, undermining crop productivity in these areas. This is partly due to lack of precise and site-specific information to guide irrigation scheduling. In most cases information on soil properties and crop water requirements is lacking and thus irrigation scheduling is done on rotational basis whether the crop needs water or not. Consequently, the amount of water stored in soil root zone for crop... E. Opolot, D. Besigye, P. Musinguzi, I.N. Alou, T.A. Basamba, G. Olupot

4. The Global Crop Nutrient Removal Database (Gcnrd): Development, Initial Analysis and Identification of Current Data Gaps

Improved understanding of the factors affecting nutrient uptake can enable production optimization, increased nutrient use efficiencies, reduced environmental footprints, and overall higher economic returns for farmers. Crop uptake is affected by genetics, soil, weather, agronomic practices, and their interactions. As such, understanding the way crop nutrient uptake varies across different regions and production environments requires the coverage of various parameters, and necessitates collaboration... C. Ludemann, A. Dobermann, N. Graff, S. Sela, M. Van loon, R. Hijbeek, M. Van ittersum