Analysis of spatial yield variability and economics of prescriptions for precision agriculture: a crop modeling approach

2000 ◽  
Author(s):  
Joel Obien Paz
Keyword(s):  
Precision Agriculture ◽  
Yield Variability ◽  
Crop Modeling ◽  
Modeling Approach

Advances in precision agriculture in south-eastern Australia. I. A regression methodology to simulate spatial variation in cereal yields using farmers' historical paddock yields and normalised difference vegetation index

2009 ◽  
Vol 60 (9) ◽  
pp. 844 ◽  
Author(s):  
P. D. Fisher ◽  
M. Abuzar ◽  
M. A. Rab ◽  
F. Best ◽  
S. Chandra
Keyword(s):  
Linear Relationship ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Yield Variability ◽  
Variable Rate ◽  
Management Options ◽  
Yield Data ◽  
Alternative Management ◽  
Financial Benefits ◽  
Normalised Difference Vegetation Index

Despite considerable interest by Australian farmers in precision agriculture (PA), its uptake has been low. Analysis of the possible financial benefits of alternative management options that are based on the underlying patterns of observed spatial and temporal yield variability in a paddock could increase farmer confidence in adopting PA. The cost and difficulty in collecting harvester yield maps have meant that spatial yield data are generally not available in Australia. This study proposes a simple, economical and easy to use approach to generate simulated yield maps by using paddock-specific relationships between satellite normalised difference vegetation index (NDVI) and the farmer’s average paddock yield records. The concept behind the approach is illustrated using a limited dataset. For each of 12 paddocks in a property where a farmer’s paddock-level yield data were available for 3–5 years, the paddock-level yields showed a close to linear relationship with paddock-level NDVI across seasons. This estimated linear relationship for each paddock was used to simulate mean yields for the paddock at the subpaddock level at which NDVI data were available. For one paddock of 167 ha, for which 4 years of harvester yield data and 6 years of NDVI data were available, the map of simulated mean yield was compared with the map of harvester mean yield. The difference between the two maps, expressed as percentage deviation from the observed mean yield, was <20% for 63% of the paddock and <40% for 78% of the paddock area. For 3 seasons when there were both harvester yield data and NDVI data, the individual season simulated yields were within 30% of the observed yields for over 70% of the paddock area in 2 of the seasons, which is comparable with spatial crop modelling results reported elsewhere. For the third season, simulated yields were within 30% of the observed yield in only 22% of the paddock, but poor seasonal conditions meant that 40% of the paddock yielded <100 kg/ha. To illustrate the type of financial analysis of alternative management options that could be undertaken using the simulated yield data, a simple economic analysis comparing uniform v. variable rate nitrogen fertiliser is reported. This indicated that the benefits of using variable rate technology varied considerably between paddocks, depending on the degree of spatial yield variability. The proposed simulated yield mapping requires greater validation with larger datasets and a wider range of sites, but potentially offers growers and land managers a rapid and cost-effective tool for the initial estimation of subpaddock yield variability. Such maps could provide growers with the information necessary to carry out on-farm testing of the potential benefits of using variable applications of agronomic inputs, and to evaluate the financial benefits of greater investment in PA technology.

scholarly journals Intensity of Precision Agriculture Technology Adoption by Cotton Producers

2011 ◽  
Vol 40 (1) ◽  
pp. 133-144 ◽  
Author(s):  
Kenneth W. Paxton ◽  
Ashok K. Mishra ◽  
Sachin Chintawar ◽  
Roland K. Roberts ◽  
James A. Larson ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Count Data ◽  
Estimation Procedure ◽  
Yield Variability ◽  
Logit Models ◽  
Factors Affecting ◽  
Level Data ◽  
Cotton Farmers ◽  
Data Estimation

Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.

Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture

2013 ◽  
Vol 101 (3) ◽  
pp. 582-592 ◽  
Author(s):  
Chenghai Yang ◽  
James H. Everitt ◽  
Qian Du ◽  
Bin Luo ◽  
Jocelyn Chanussot
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Precision Agriculture ◽  
Crop Growth ◽  
Growth And Yield ◽  
Yield Variability

scholarly journals Integration of Precision Agriculture and Systems Modeling in Pistachio

HortScience ◽  
2005 ◽  
Vol 40 (4) ◽  
pp. 1141E-1142
Author(s):  
Todd Rosenstock ◽  
Patrick Brown
Keyword(s):  
Precision Agriculture ◽  
Yield Potential ◽  
Crop Modeling ◽  
Alternate Bearing ◽  
Female Ratio ◽  
Tree Crops ◽  
Environmental Consequences ◽  
Annual Crops ◽  
Male Female Ratio ◽  
Harvest Data

Alternate bearing exerts economic and environmental consequences through unfulfilled yield potential and fertilizer runoff, respectively. We will discuss a systematic biological–statistical modeling management integration approach to address the concert of mechanisms catalyzing alternate bearing. New engineering technologies (precision harvesting, spatially variable fertigation, and mathematical crop modeling) are enabling optimization of alternate bearing systems. Four years of harvest data have been collected, documenting yield per tree of an 80-acre orchard. These results have shown variability within orchard to range from 20–180 lbs per tree per year. Results indicate irregular patterns not directly correlated to previous yield, soil, or tissue nutrient levels, or pollen abundance. Nor does significant autocorrelation of high or low yields occur throughout the orchard, suggesting that genetically dissimilar rootstocks may have significant impact. The general division of high- and low-yielding halves of the orchard may infer a biotic incongruency in microclimates. This orchard does not display a traditional 1 year-on, 1 year-off cyclic pattern. Delineation of causal mechanisms and the ability to manage effectively for current demands will empower growers to evaluate their fertilization, irrigation, male: female ratio, site selection, and economic planning. In comparison to annual crops, the application of precision agriculture to tree crops is more complex and profitable. When applied in conjunction, the aforementioned methods will have the ability to forecast yields, isolate mechanisms of alternate bearing, selectively manage resources, locate superior individuals, and establish new paradigms for experimental designs in perennial tree crops.

Improving crop modeling to better simulate maize yield variability under different irrigation managements

2022 ◽  
Vol 262 ◽  
pp. 107429
Author(s):  
Olufemi P. Abimbola ◽  
Trenton E. Franz ◽  
Daran Rudnick ◽  
Derek Heeren ◽  
Haishun Yang ◽  
...  
Keyword(s):  
Maize Yield ◽  
Yield Variability ◽  
Crop Modeling

scholarly journals Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications

2017 ◽  
Vol 10 (4) ◽  
pp. 1403-1422 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Crop Models ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that other modeling groups can also test their model performance against the reference data and the GGCMI benchmark.

scholarly journals Global Gridded Crop Model evaluation: benchmarking, skills, deficiencies and implications

2016 ◽  
Author(s):  
Christoph Müller ◽  
Joshua Elliott ◽  
James Chryssanthacopoulos ◽  
Almut Arneth ◽  
Juraj Balkovic ◽  
...  
Keyword(s):  
Time Series ◽  
Model Evaluation ◽  
Reference Data ◽  
Model Performance ◽  
Global Scale ◽  
Crop Model ◽  
Yield Variability ◽  
Crop Modeling ◽  
Model Intercomparison ◽  
Simulation Results

Abstract. Crop models are increasingly used to simulate crop yields at the global scale, but there so far is no general framework on how to assess model performance. We here evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that GGCMs show mixed skill in reproducing time-series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producer countries by many GGCMS and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all crop modelers so that also other modeling groups can test their model performance against the reference data and the GGCMI benchmark.

Sign in / Sign up

Export Citation Format

Share Document