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.

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Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized.

Introduction: The FAO-56 Penman-Monteith (PM) is one of the most solid and commonly used methods for estimating reference evapotranspiration (ETo); however, it requires meteorological data that are not always available, so an alternative is the use of reanalysis data. Objective: To estimate the error that the NASA-POWER (NP) system data can generate in the ETo of the Comarca Lagunera, Mexico. Methodology: Daily and decadal average ETo were estimated in five different ways. In each case, a different method was used to estimate ETo (FAO-56 PM or Hargreaves and Samani [HS]) and a different meteorological data source (measured, NP data or combination of both). Results: NP data can be used to provide temperature, solar radiation and relative humidity variables, but not wind speed. The NP data overestimate the measured ETo, an RMSE of 1.15 and 0.89 mm∙d-1 was found for daily and decadal periods, respectively. Limitations of the study: A grid error analysis could not be carried out because the number of stations is limited. Originality: The use of reanalysis data to estimate ETo has not been analyzed locally. Conclusion: When measured data are not available, NP data and the HS equation can be used. When using the FAO-56 PM method and NP data, the in situ wind speed must be available.

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).

An accurate irrigation scheduling methodology is necessary in crops with high water stress sensitivity and production cost. This involves the estimation of the scheduling parameters related with crop water requirements during phenological stages under different irrigation application systems. This paper presents several models to represent the parameters of irrigation scheduling based on growing degree days (GDD) such as: management allowed soil water depletion (MAD), rooting depth (R d ) and crop coefficient (K c ). The proposed models were applied accurately to schedule irrigation in two commercial fields of potato under two irrigation methods: surface and sprinkler irrigation. Results show that the model predicted irrigations in a consistent and logical manner. The proposed models are versatile, feasible and easily implemented in irrigation scheduling computer programs.

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