Lucerne improves some sustainability indicators but may decrease profitability of cropping rotations on the Jimbour Plain

2005 ◽  
Vol 45 (6) ◽  
pp. 651 ◽  
Author(s):  
R. B. Murray-Prior ◽  
J. Whish ◽  
P. Carberry ◽  
N. Dalgleish
Keyword(s):  
Standard Deviation ◽  
Simulation Model ◽  
Stochastic Dominance ◽  
Sustainability Indicators ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Efficiency Measures ◽  
Long Run ◽  
Gross Margins ◽  
Group Rotations

Long-run rotational gross margins were calculated with yields derived from biophysical simulations in a crop simulation model over a period of 100 years and prices simulated in @Risk based on subjective triangular price distributions elicited from the Jimbour Plain farmer group. Rotations included chickpeas, cotton, lucerne, sorghum, wheat and different lengths of fallow. The aim was to assess the profitability of rotations with and without lucerne. Output presented to the farmers included mean annual gross margins and distributions of gross margins with box and whisker plots found to be suitable. Mean–standard deviation and first- and second-degree stochastic dominance efficiency measures were also calculated. The paper outlines a method for combining biophysical and price simulations that can be understood by farmers. Including lucerne in the rotations improved some sustainability indicators but reduced profitability.

Optimizing Sugarcane Planting Windows Using a Crop Simulation Model at the State Level

2021 ◽  
Author(s):  
Jéssica Sousa Paixão ◽  
Derblai Casaroli ◽  
João Carlos Rocha dos Anjos ◽  
José Alves Júnior ◽  
Adão Wagner Pêgo Evangelista ◽  
...  
Keyword(s):  
Simulation Model ◽  
State Level ◽  
The State ◽  
Crop Simulation Model ◽  
Crop Simulation

scholarly journals Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

2020 ◽  
Vol 12 (13) ◽  
pp. 2099
Author(s):  
Mongkol Raksapatcharawong ◽  
Watcharee Veerakachen ◽  
Koki Homma ◽  
Masayasu Maki ◽  
Kazuo Oki
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Rice Yield ◽  
Drought Monitoring ◽  
Weather Data ◽  
Area Index ◽  
Drought Impact ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Rice Yields

Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

Application of a Maize Crop Simulation Model in the Central Region of Malawi

1995 ◽  
Vol 31 (2) ◽  
pp. 213-226 ◽  
Author(s):  
P. K. Thornton ◽  
A. R. Saka ◽  
U. Singh ◽  
J. D. T. Kumwenda ◽  
J. E. Brink ◽  
...  
Keyword(s):  
Simulation Model ◽  
Field Experiments ◽  
Data Sets ◽  
Management Options ◽  
Maize Crop ◽  
Maize Production ◽  
Policy Makers ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Using Data

SUMMARYA computer crop simulation model of the growth and development of maize was validated using data sets obtained from field experiments run at various sites in the mid-altitude maize zone of central Malawi between 1989 and 1992. The model was used to provide information concerning management options such as the timing and quantity of nitrogen fertilizer applications and to quantify the weather-related risks of maize production in the region. It was linked to a Geographic Information System to provide information at a regional level that could ultimately be of value to policy makers and research and extension personnel.

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