Using spatial information systems to improve water management in Mexico

Ojeda-Bustamante, Waldo; González-Camacho, Juan Manuel; Sifuentes-Ibarra, Ernesto; Isidro, Esteban; Rendón-Pimentel, Luis

doi:10.1016/j.agwat.2006.11.002

Cited by 13 publications

(11 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These are managed directly by the agricultural producers. On the other hand, there are 83 irrigation districts covering an area of 3.5 million ha (Ojeda-Bustamante et al, 2007). These were formerly managed by the federal government and turned over to 474 water user associations (also called "irrigation modules").…”

Section: Case Studymentioning

confidence: 99%

“…The former included records at farm level regarding planting area, crop species and variety, day and quantity of irrigated water, starting and ending sowing dates and crop's yield. Such data were obtained from the Spriter-GIS system, a tool for irrigation management and statistical monitoring used in the module (Ojeda-Bustamante et al, 2007). The second dataset was composed of climatological variables such as rainfall, solar radiation, and temperatures.…”

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Predictive ability of machine learning methods for massive crop yield prediction

González-Sanchez

Frausto–Solís

Ojeda-Bustamante

2014

Span. j. agric. res.

Self Cite

193

View full text Add to dashboard Cite

Crop yield prediction (CYP) is a major problem in agriculture. Starting each growing season, agricultural planners require estimating the yield for all the involved crops (Frausto-Solis et al., 2009). Regrettably, CYP is difficult because it depends on many interrelated factors (Liu et al., 2001;Marinković et al., 2009). Moreover, yield is also affected by farmer decisions (such as applied irrigations, pest and fertilizers applications, crop rotation, and land preparation) and incontrollable factors (such as weather, subsidies and market). As stated by Ruß (2009), yield prediction traditionally has relied on farmers' long-term experience for specific fields, crops and climate conditions, which can be inaccurate. Simple estimators, such as the average of several previous yields or the last obtained yield, are also used. Nevertheless, crop yield varies spatially and temporally with a non-linear behavior (Liu et al., 2001;Drummond et al., 2003;Schlenker & Roberts, 2006) AbstractAn important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML) is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS), root relative square error (RRSE), normalized mean absolute error (MAE), and correlation factor (R). Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5-Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91), the lowest RRSE errors (79.46% and 79.78%), the lowest average MAE errors (18.12% and 19.42%), and the highest average correlation factors (0.41 and 0.42). Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning.

show abstract

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

Predictive ability of machine learning methods for massive crop yield prediction

González-Sanchez

Frausto–Solís

Ojeda-Bustamante

2014

Span. j. agric. res.

Self Cite

193

View full text Add to dashboard Cite

show abstract

“…Spatial tools for water resources management in Mexico are being developed and applied, such as canal irrigation operations and management (Ojeda-Bustamante et al 2007) and Global Positioning System locational inventorying of infrastructure for operational purposes. However, despite innovation with spatially explicit groundwater models for decision support, for example, in the central state of Guanajuato (Sandoval-Minero 2001), the Mexican government's official estimates of groundwater availability and depletion are estimated based on aquifer-wide average water balances of supply (recharge) and demand (pumping).…”

mentioning

confidence: 99%

Groundwater Rights in Mexican Agriculture: Spatial Distribution and Demographic Determinants

Scott

Dall’erba

Caravantes

2010

The Professional Geographer

View full text Add to dashboard Cite

“…The Collembola community from two agroecosystems with contrasting irrigation types were compared in the State of Hidalgo (Cutz-Pool et al, 2007) stating that soil properties such as pH, electrical conductivity (EC), organic matter (OM), and exchangeable contents of cations (Mg 2+ , Na + ) appear to affect the composition and abundance of these communities. A computer tool allowing the dynamic generation of digital map reports was developed and transferred to water user associations (WUA) of irrigation districts (Ojeda-Bustamante et al, 2007).…”

Section: Water and Soil Quality In Arid And Semi-arid Regions In Latimentioning

confidence: 99%

Environmental Effects of Irrigation in Arid and Semi-Arid Regions

Fernández-Cirelli

Arumí²,

Rivera³

et al. 2009

Chilean J. Agric. Res.

View full text Add to dashboard Cite

This article reviews the state of the art with respect to the environmental effects of irrigated agriculture on water and soil quality in arid and semi-arid regions on a field scale. Information is scarce and fragmentary. Examples in selected areas of other arid and semi-arid regions in the world clearly show the importance of studying the environmental impact of irrigation practices on water and soil quality. Studies mainly refer to waterlogging and salinization. As regards agrochemicals, fertilizers have been taken into account through nitrate leaching. The impact of micropollutants such as pesticides and heavy metals on water and soil quality is studied mainly by modeling. Pharmaceutical compounds emerge as pollutants when wastewater is used for irrigation without any previous treatment, situation which is now the object of new studies. There is an obvious need to study in-depth knowledge related to appropriate technologies for the use, treatment, and reuse of wastewater which is a valuable resource in arid and semi-arid regions.

show abstract

Using spatial information systems to improve water management in Mexico

Cited by 13 publications

References 13 publications

Predictive ability of machine learning methods for massive crop yield prediction

Predictive ability of machine learning methods for massive crop yield prediction

Groundwater Rights in Mexican Agriculture: Spatial Distribution and Demographic Determinants

Environmental Effects of Irrigation in Arid and Semi-Arid Regions

Contact Info

Product

Resources

About