Predictive Irrigation Scheduling Modeling in Nurseries

Water requirement allocation plays an important role in modern farming management. Evapotranspiration-based irrigation controllers can ideally provide irrigation according to the water requirements of the plant. This chapter describes predictive irrigation scheduling in nurseries with multiple crop species and high-frequency water requirements under limited resources. Based on historical data, time-series analysis is used to forecast evapotranspiration, an essential element in water balance equation. An algorithm based on a hierarchical research including dispatching priority rules and taking into account crop characteristics, available water, and constraints of the hydraulic network is proposed to predict irrigation schedules, with the objective of minimizing crop’s water stress periods and optimizing resource materials. Simulation results with different climatic conditions show on the one hand the ability of the time-series model to forecast potential evapotranspiration, and on the other hand that, given a typical nursery, the proposed predictive approach of irrigation scheduling compared to the non-predictive approach makes it possible to prevent crop’s water stress

Heuristic simulation software

Presented at Meeting irrigation demands in a water-challenged environment: SCADA and technology: tools to improve production: a USCID water management conference held on September 28 - October 1, 2010 in Fort Collins, Colorado.Includes bibliographical references.A modern computer-based simulation tool in the form of a game for on-farm water management has been developed for application in training events for farmers, irrigators, irrigation extension specialists, and students. This training tool can be used to analyze both strategic and operational issues related to the management of on-farm water resources, and automatic analysis of the results to provide feedback to the trainees. It utilizes an interactive framework, thereby allowing the trainee (or player) to develop scenarios and test alternatives in a convenient, risk-free environment. It employs heuristic capabilities in a simulation approach for modeling all of the important aspects of on-farm water management that are essential to effective planning. The daily soil water balance, crop phenology, root development, and a seven-day weather forecast, can be monitored by the player throughout the simulated growing season. Different crop types, water delivery methods, and irrigation methods are made available to the player. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide potentially more challenging game play. Scoring and recommendations are provided at the end of the game, based on the management decisions made by the player

On-Farm Water Management Game With Heuristic Capabilities

A modern computer-based simulation tool (WaterMan) in the form of a game for on-farm water management was developed for application in training events for farmers, students, and irrigators. The WaterMan game utilizes an interactive framework, thereby allowing the user to develop scenarios and test alternatives in a convenient, risk-free environment. It includes a comprehensive soil water and salt balance calculation algorithm. It also employs heuristic capabilities for modeling all of the important aspects of on-farm water management, and to provide reasonable scores and advice to the trainees. Random events (both favorable and unfavorable) and different strategic decisions are included in the game for more realism and to provide an appropriate level of challenge according to player performance. Thus, the ability to anticipate the player skill level, and to reply with random events appropriate to the anticipated level, is provided by the heuristic capabilities used in the software. These heuristic features were developed based on a combination of two artificial intelligence approaches: (1) a pattern recognition approach; and (2) reinforcement learning based on a Markov Decision Processes approach, specifically, the Q-learning method. These two approaches were combined in a new way to account for the difference in the effect of actions taken by the player and action taken by the system on the game world. The reward function for the Q-learning method was modified to reflect the anticipated type of the WaterMan game as what is referred to as a partially competitive and partially cooperative game. Twenty-two different persons classified under three major categories (1) practicing farmers; (2) persons without an irrigation background; and (3) persons with an irrigation background, were observed while playing the game, and each of them filled out a questionnaire about the game. The technical module of the game was validated in two ways: through conducting mass balance calculations for soil water content and salt content over a period of simulation time, and through comparing the WaterMan technical module output data in calculating the irrigation requirements and the use of irrigation scheduling recommendations with those obtained from the same set of input data to the FAO CropWat 8 software. The testing results and the technical validation outcomes demonstrate the high performance of the WaterMan game as a heuristic training tool for on-farm water management

Modeling canopy-induced turbulence in the Earth system: a unified parameterization of turbulent exchange within plant canopies and the roughness sublayer (CLM-ml v0)

Land surface models used in climate models neglect the roughness sublayer and parameterize within-canopy turbulence in an ad hoc manner. We implemented a roughness sublayer turbulence parameterization in a multilayer canopy model (CLM-ml v0) to test if this theory provides a tractable parameterization extending from the ground through the canopy and the roughness sublayer. We compared the canopy model with the Community Land Model (CLM4.5) at seven forest, two grassland, and three cropland AmeriFlux sites over a range of canopy heights, leaf area indexes, and climates. CLM4.5 has pronounced biases during summer months at forest sites in midday latent heat flux, sensible heat flux, gross primary production, nighttime friction velocity, and the radiative temperature diurnal range. The new canopy model reduces these biases by introducing new physics. Advances in modeling stomatal conductance and canopy physiology beyond what is in CLM4.5 substantially improve model performance at the forest sites. The signature of the roughness sublayer is most evident in nighttime friction velocity and the diurnal cycle of radiative temperature, but is also seen in sensible heat flux. Within-canopy temperature profiles are markedly different compared with profiles obtained using Monin–Obukhov similarity theory, and the roughness sublayer produces cooler daytime and warmer nighttime temperatures. The herbaceous sites also show model improvements, but the improvements are related less systematically to the roughness sublayer parameterization in these canopies. The multilayer canopy with the roughness sublayer turbulence improves simulations compared with CLM4.5 while also advancing the theoretical basis for surface flux parameterizations

WatBal- An Integrated Water Balance Model for Climate Impact Assessment of River Basin Runoff

A water balance model combined with the Priestly-Taylor method for computing potential evapotranspiration has been developed as an integrated tool for modeling the response of river basins to potential climate change. The system was designed within the EXCEL 5.0 spreadsheet environment making use of the Visual Basic programming language. The model is simple to use and takes advantage of IIASA's mean monthly hydrologic data base (Leemans and Cramer, 1992). The model environment is described and two case studies are shown using the model

WETLANDS, WILDLIFE, AND WATER QUALITY: TARGETING AND TRADE OFFS

Cost-effective targeting of conservation activities has only recently been addressed by economists. Most work to date has focused on finding the best locations to set aside land for the protection of biodiversity. An economic approach to the problem, where biodiversity reserve networks are delineated to maximize the number of species protected subject to a budget constraint, has been shown to be much more cost-effective than the standard approach, where reserve networks are delineated subject to an area constraint, ignoring differences in costs across sites. This paper is among the first to use spatially explicit models of production functions for ecosystem services in an optimization framework for prioritizing sites for wetlands restoration. Tradeoffs between two classes of environmental benefits from wetlands restoration, habitat, and water quality were assessed in the Central Valley of California. Habitat benefits were estimated by a count regression model that relates breeding mallard abundances to the configuration of land use types in the study area, and water quality benefits were estimated by a spatially distributed model of nonpoint source pollution and nutrient attenuation in wetlands. Two decision scenarios were analyzed. In the first scenario the optimal configuration of restoration activity was determined for a small watershed, and in the second scenario sites were selected from those offered for enrollment in an easement program throughout the valley. The results reveal the potential for gains in effectiveness from spatial targeting, and they suggest that there will be substantial tradeoffs between environmental benefits. Maximizing habitat quality in the small watershed yielded a 34% increase in mallard abundance and a 3% decrease in nitrogen loads to the river. In contrast, maximizing water quality resulted in a 25% decrease in nitrogen loads and a 2% increase in mallard abundance. Qualitatively similar results were obtained when sites were selected from a set of offered sites throughout the valley, but the tradeoffs were not as severe. The results also suggest that at traditional funding levels the Wetlands Reserve Program in California could reduce nitrogen loads to rivers by approximately 29,000 kg and increase total mallard abundance in the breeding season by approximately 150 individuals throughout the Central Valley in a given year.Resource /Energy Economics and Policy,

Modeling pan evaporation using Gaussian Process Regression, K-Nearest Neighbors, Random Forest, and Support Vector Machines: Comparative analysis

Evaporation is a very important process; it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, evaporation is considered as a complex and nonlinear phenomenon to model. Thus, machine learning methods have gained popularity in this realm. In the present study, four machine learning methods of Gaussian Process Regression (GPR), K-Nearest Neighbors (KNN), Random Forest (RF) and Support Vector Regression (SVR) were used to predict the pan evaporation (PE). Meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W), and sunny hours (S) collected from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The results of this study showed that at Gonbad-e Kavus, Gorgan and Bandar Torkman stations, GPR with RMSE of 1.521 mm/day, 1.244 mm/day, and 1.254 mm/day, KNN with RMSE of 1.991 mm/day, 1.775 mm/day, and 1.577 mm/day, RF with RMSE of 1.614 mm/day, 1.337 mm/day, and 1.316 mm/day, and SVR with RMSE of 1.55 mm/day, 1.262 mm/day, and 1.275 mm/day had more appropriate performances in estimating PE values. It was found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W and S had the most accurate predictions and were proposed for precise estimation of PE. The findings of the current study indicated that the PE values may be accurately estimated with few easily measured meteorological parameters