Estimating potato leaf area index for specific cultivars

1997 ◽  
Vol 40 (3) ◽  
pp. 251-266 ◽  
Author(s):  
R. Gordon ◽  
D. M. Brown ◽  
M. A. Dixon
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Potato Leaf ◽  
Area Index

Improving Seed Potato Leaf Area Index, Stomatal Conductance and Chlorophyll Accumulation Efficiency through Irrigation Water, Nitrogen and Phosphorus Nutrient Management

2016 ◽  
Vol 4 (1) ◽  
pp. 127 ◽  
Author(s):  
Gathungu Geofrey Kingori ◽  
Aguyoh Joseph Nyamori ◽  
Isutsa Dorcas Khasungu
Keyword(s):  
Stomatal Conductance ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Seed Potato ◽  
Irrigation Water ◽  
Potato Leaf ◽  
Area Index ◽  
Water Rate ◽  
P Application

A study was conducted in a Rainshelter (RTrial) at Horticultural Research and Teaching Farm, Egerton University to determine the effect of integration of irrigation water, nitrogen (N) and phosphorus (P) application on seed potato leaf area index (LAI), stomatal conductance and chlorophyll content. The treatments arranged in a split-split plot layout in a completely randomised block design, consisted of three irrigation water rates (40%, 65% and 100% field capacity), four N rates (0, 75, 112.5 and 150 kg N/ha) supplied as urea (46% N), and four P rates (0, 50.6, 75.9, 101.2 kg P/ha) supplied as triple superphosphate, replicated three times and repeated once. During the growth leaf area, stomatal conductance, and chlorophyll content were measured. Data collected were subjected to analysis of variance and significantly different means separated using Tukey’s Studentized Range Test at P≤0.05. Leaf area index was greater with high irrigation water at 100%, N at 150 kg N/ha and P at 101.2 kg P/ha, which was 2.6 and 1.3 at 51 days after planting (DAP) and 3.5 and 3.1 at 64 DAP. Furthermore, low irrigation water rate at 40% together with low N and P rates of 0 kg N/ha and 0 kg P/ha had the least LAI, which was 0.28 and 0.19 at 51 DAP and 0.28 and 0.24 at 64 DAP both in RTrials I and II, respectively. Subjecting potato to 100% compared to 40% irrigation rate increased stomatal conductance at 87 days after planting (DAP) by 32.82 and 31.99 mmolm⁻²s⁻¹, leaf chlorophyll content index by 16.2 and 16.5, 19.8 and 19.6, and 15 and 20.3, when integrated with high compared with low N and P application rates at 59, 73 and 87 DAP, in RTrials I and II respectively. Irrespective of N and P rates LAI, stomatal conductance and chlorophyll content were significantly greater with high irrigation water at 100% followed by 65% and was lowest with 40% irrigation water rate.

Non-destructive estimation of potato leaf area index using a fish-eye radiometer

1994 ◽  
Vol 37 (4) ◽  
pp. 393-402 ◽  
Author(s):  
R. Gordon ◽  
D. M. Brown ◽  
M. A. Dixon
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Potato Leaf ◽  
Area Index ◽  
Non Destructive ◽  
Fish Eye

scholarly journals Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

2021 ◽  
Vol 13 (16) ◽  
pp. 3069
Author(s):  
Yadong Liu ◽  
Junhwan Kim ◽  
David H. Fleisher ◽  
Kwang Soo Kim
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Satellite Data ◽  
Growing Season ◽  
Environmental Data ◽  
Area Index ◽  
Current Season ◽  
Wide Range

Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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