nitrogen balance in a range of locations and management systems), a more detailed crop model will be preferred. Both approaches are legitimate in certain circumstances. As a result of the prescribed form of the generic template, crop modellers who want to take different approaches have not used it. Instead they have integrated alternative models into APSIM, resulting in contrasting code bases for crop models and a maintenance burden for the software team. New developments in the software industry (David et al., 2013), as well as lessons learned over the last 10 years from building models in the GCROP and PLANT templates (Holzworth and Huth, 2009b; Holzworth et al., 2010), have made an update to the generic crop template possible. Newer computer programming languages (e.g. C#) have the ability to inspect source code at run- time, extracting metadata about the code. This ability, called re ﬂ ection or introspection (Rahman et al., 2004), can be used by a model framework to locate required functionality (classes) and determine their data requirements (inputs). This means that classes can be developed independently and models can be constructed at run time from executable mark-up language (XML) ﬁ les that specify how to assemble and parameterise classes. The updated template is called the Plant Modelling Framework (PMF). This has been in development over the past 3 years aiming to achieve a number of key design goals:
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Figure 4 Use and misuse of crop models, based on 178 model results published in climate change impacts studies between 1994 and 2014, and disaggregated by model type. (A) Fraction of results that perform simulations at the scale for which the model was designed; (B) fraction of results (at scales other than field) for each model type that use multiple parameter sets (i.e. account for parametric uncertainty); and (C) fraction of studies that state model evaluation procedures for their locations or areas of interest. Model types are as follows: CSM-FS: field-scale crop growth simulation model; CSM-RS: regional-scale crop growth simulation model; E/S: empirical and/or statistical. Note that field scale models are used above field scale in roughly 50 % of the cases.
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Integrated assessment models (IAMs) may be expected to deliver frameworks for interconnected risks; however the use of crop models within IAMs is at a relatively early stage (Ewert et al., 2015). Further, IAMs may not be the best tool to assess the range of trade-oﬀs and synergies that are important to food systems. The complexity of the inter-related set of climate change and food security risks and responses has led to them being labelled a “wicked problem” requiring a range of approaches (Vermeulen et al., 2013). Food security targets are not so- lely a matter of increasing yield, but also of improving food access, quality and diversity. There may be direct yield trade-o ﬀ s involved in actions and activities that contribute towards food security (Campbell et al., 2016). The integration of local knowledge and the input of social scientists within interdisciplinary modelling research can contribute to the identiﬁcation and outlining of realistic scenarios of socio-technical change, crop-climate indices, or of model output priorities (i.e. not solely yield Herrero et al., 2015, Campbell et al., 2016). The insights gained may inform the design of models and modelling studies that go beyond conventional projections of yield and yield response and are designed to analyse trade-o ﬀ s (Wessolek and Asseng, 2006), determine least regrets options, or inform multi-criteria analyses (Hallegatte, 2009, Challinor et al., 2010).
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The near parallel development of remote sensing and crop growth models has driven some scientists to recognise the usefulness of remote sensing in crop management, which lead to the development of combined applications . Low-resolution remote sensing data have been broadly used in crop yield forecasting and monitoring. High temporal frequency combined with broad spatial coverage and low cost, has made these data a preferred choice for national and regional scale applications. Since the beginning, many scientists have used the available satellite data to retrieve canopy state variables over large areas. Among them, LAI is being monitored frequently across various scales and resolutions, and together with actual evapotranspiration and soil moisture estimations from thermal satellite images [67,68], they have been used with crop models. Remote sensing data are mostly used to determine light interception (e.g., LAI or fAPAR), and provide the spatial information of the actual growth status of the crop. Most of the studies examine the assimilation of LAI as a variable for crop yield estimation but there are other factors affecting crop development, such as water stress, nutrient supply and pests . Neiring et al.  assimilated remotely sensed LAI and soil moisture into DSSAT-CERES model and their results showed that in order to combine remote sensing data with crop models for estimating yield at single-season time scales, it will be necessary to modify our interpretation of crop development. They suggest the investigation of methods and other ancillary data for correlating leaf development with grain development directly. LAI and Evapotranspiration (ET) express two important crop processes, LAI simulates crop canopy development, which affect light interception and photosynthesis and ET reflects the available water to support crop growth. Improving the simulation of these variables is essential for accurate crop yield estimations . Furthermore, these variables could be used to spatially calibrate the model by locally estimating the missing information in model parameters .
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that are impacted in the plant, from cellular injury and damage (that can result in visible injury and alterations to photosynthesis and stomatal conductance) through to leaf level impacts on physiology and leaf senescence and ultimately to alterations in whole plant canopy and root systems that will affect biogeochemical cycling within the plant. We consider these processes from the viewpoint of developing crop growth models that are capable of incorporating key ozone impact processes within modelling structures that asses crop growth under a variety of different stresses. This would provide a dynamic assessment of the impact of ozone on crop growth within the context of other key variables considered important in determining crop growth and yield. We consider the ability to achieve this through an assessment of the different types of crop model (e.g. empirical, radiation use efficiency, and photosynthesis based crop growth models. Finally, we show how international activities such as the AgMIP (Agricultural Modelling and Improvement Intercomparison Project) could provide a network of crop growth modellers to assess the capabilities of different crop models to simulate the effects of ozone and other stresses to improve future regional and global risk assessments.
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To create a link between DYMEX and APSIM, the DYMEX simulation engine was incorporated into APSIM. This technically integrated approach (Knapen et al., 2013) was chosen over the al- ternatives (incorporating an APSIM simulation into the DYMEX simulator or writing a separate piece of linking software) for two reasons: ﬁ rstly, this approach enables multi-point models (the ability to simultaneously simulate multiple points in space and the interactions between them, thus allowing the simulation of weed patch dynamics or disease movement between points) that link agro-ecological and population sub-models and secondly, the input and output facilities in APSIM are more suited to running and interpreting detailed biophysical models. A software interface to the DYMEX simulation engine (without its graphical user interface) was developed to implement DYMEX as a CMP-compliant compo- nent (Moore et al., 2007). Because APSIM simulations use the CMP, the DYMEX component executes with the rest of the APSIM simulation, it accepts information from other modules in the simulation (e.g. weather data drawn from standard APSIM climate ﬁ les) and sends information (e.g. rust lesion growth) to other models (Fig. 1). The component interface was written to allow any model constructed with the DYMEX builder to be linked into APSIM.
Here we showed that the value, in terms of increases in profits and reductions in downside risks, from identifying optimum GxM combinations using a crop simulation model and a seasonal climate forecast was significant across all tested locations and soil conditions i.e. soil PAWC values (Table 4). The value in skill depended on the baseline for the comparison: When current farmers’ practice was used as the baseline, linking APSIM sorghum and POAMA-2 increased average profits by 143 AU$ ha −1 and reduced or even eliminated down side risk. When the
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Understanding crop response towards projected changes in climate is required for formulating adaptation strate- gies and policy. Crop simulation models help to understand crop bio dynamism under changing climatic condi- tions. The calibration and evaluation of crop models has given more insight into the influence of variability in temperature and rainfall regimes on sorghum in central Tanzania. Considering future climates up to 2050s, productivity of grain sorghum will be diversely affected due to the differences in the GCMs projections in tem- perature and rainfall. Increase of 5% - 23.0% in sorghum yields is projected by both crop models under four GCMs. Contrasting results also have been observed with the other GCMs. The simulation results under adjust- ment of crop growing duration under future projections show increase of median sorghum yields according to CERES-sorghum model. We conclude that with proper calibrations and evaluations, crop models can reasonably predict future sorghum yields in the study area and other area with similar environments. This study, quantita- tively ascertains the current promotion of sorghum production as an appropriate crop to be grown in the study area, instead of the continued reliance on maize as a staple crop which is currently at high risk. Modifying man- agement practices through the deliberate choice between improved cultivars and local landraces can be feasible options depending on GCM for enhancing the adaptive capacity of smallholder farmers in central Tanzania, to ensure increased production of the crop for enhanced food security and livelihoods.
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Additional discussion of the C3MP data set in the con- text of other AgMIP modeling efforts is presented in Ruane et al. (2017). One relevant point to this work is that, while C3MP spatial coverage is not spatially uniform or produc- tion weighted for any of the crops under consideration, sites for many of the major production regions are represented for each crop (Fig. 2). A major advantage of using site-specific crop models run voluntarily by experts is that the individual baseline runs at each site have been configured against local information in the historical period. However, the application of crop yield response from these sites to estimate response in any given grid cell with temperature and precipitation data is imperfect by its methodological nature. Yet, this extension is necessary for use with GCAM: gridded yield changes for a subset of crops must be aggregated and converted to yield impact multipliers for each GCAM commodity in each land unit, defined as water basins in GCAM (Calvin et al., 2019). Given the size and details of the C3MP data set, produc- tion groups were formed based on two latitude zones as a way to account for baseline local temperature (which is im- portant in addition to the change from local temperature) without having to eliminate the many valid C3MP sites that could not report local weather data due to data gaps or local government restrictions. As this breakdown already results in some production groups with small sample sizes (see Table 1 and Sect. 3.1.1), further spatial disaggregation of production group is unjustified in this data set. While this means there will be limited spatial granularity in yield response func- tions, there can still be appreciable spatial granularity in yield changes due to variation in the gridded fields of temperature
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The Earth’s land resources are finite, whereas the number of people that the land must support increases rapidly, this situation has been a great concern in the area of agriculture. Crop produc- tion must be increased to meet the rapidly growing food demands through sophisticated agricul- tural processes, while it is important to protect other natural resources and the environment. New agricultural research is needed to provide additional information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate change around the world. Therefore many researchers have over the years shown interest in finding ways to estimate the yield of crops before harvest. This paper reviews some of the crop growth models that have been successfully developed and used over time. The applications of crop growth models in agricultural meteorology, the role that climate changes play in these models and few of the successfully used crop models in agro-meteorology are also discussed in detail.
---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Agriculture plays a vital role in India’s economy. Over 58 percent of the rural households depend on agriculture as their principal means of livelihood. However, the farmers of India have been facing a lot of real time challenges like crop diseases. They are major threat to food security, but their immediate identification remains difficult in many places due to the lack of proper facilities. However, increasing smartphone penetration along with advancement in computer vision that are made possible via deep learning, have paved the way for smartphone-assisted disease diagnosis. Using a public dataset of 16,471 images of diseased and healthy plant leaves collected under controlled conditions, we train a deep convolutional neural network to identify 8 crop species and 15 labelled diseases. The trained model achieves an accuracy of 99% on the testing dataset.
No doubt, various studies concerning forecasting models do exist in the available literature but they are limited in scope to some extent, neglecting the assumptions of OLS estimation. This research study is designed to develop forecasting models for acreage and production of wheat crop for Chakwal district of Rawalpindi region keeping in view the assumptions of OLS estimation. This study will certainly play a key role in the development of wheat area and production forecasting models for other districts of the region.
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Water utilization by crops, namely tomato, within greenhouses is one of the most significant factors in determining yield. Daily water consumption by tomato plants were calculated, to determine the actual evapotranspiration and transpiration rates, as well as the incorporation measurement of climatic variables and weekly determinations of fresh and dry weights of the plants. Estimations of daily evapotranspiration and transpiration rates by the tomato plants were calculated using multiple regression models. The best models for estimating, both the evapotranspiration and transpiration, had adjusted R 2 values greater than 0.9. Using the
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To see the impact of functional form on MTES, The Cobb Douglas and Translog functional form dummy variables are considered for estim- ation. In all models the functional form results reflect the diverse trends. The omitted category is other functional forms. The DCobb variable in methodological and provincial model shows that Cobb-Douglas functional form attained 0.8198 and 0.1040 units higher MTES compared to other functional forms, respec- tively. But in Crop specific model, it shows negative but insignificant impact on MTES. The Translog functional form in all models shows that translog functional form acquired higher MTES compared to other functional forms. But it is statistically insignificant. Hence, it emulates the fact that the translog functional form effect on MTES is rather undistinguishable. Conseq- uently, as compared to the other functional forms Cobb Douglas and Translog functional forms yield higher efficiency score. According to Greene (2002) and Thiam et al. (2001) there is no coherent justi-fication following these results. This result is in line with Ahmad and Bravo-Ureta (1996), Resti (2000) Thiam (2003) and Lopez and Bravo-Ureta (2008).
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RESULTS AND DISCUSSION The crop growth model DAISY was calibrated in several steps. The ﬁ rst step was to approximate the conditions of the observed phenological phases (ﬂ owering and maturity) to the modeled phenological phases (Fig. 4a). The parameters for the length of the vegetative and reproductive development stages were modiﬁ ed in the DAISY basic settings. The crop growth model DAISY simulated the gradual phenological development in diﬀ erent soil-climate locations very well. At lower altitudes (Lednice and Věrovany), the onset of barley’s phenological phases of ﬂ owering and maturity was earlier, thanks to the early onset of suitable conditions for sowing (Fig. 4a). The second step of calibration was to compare the observed yields with the yields that were simulated by DAISY. In this case, the sensitivity of the model to water stress had to be adjusted. Graphical representations of the modeled and simulated yields can be found in Fig. 4b. The obtained values of the RMSE and MBE can be found in Tab. IV.
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in the article, we are analyzing the multi-layer neural network regressive model which has been used for solving the problem of the yield of onion, the type Brown imperial Spanish, whilst it is compared with the regressive model applied in the same task. To determine the relationship between the yield of the crop and the sowing density or the plantation density, we have suggested empirical non-linear re- gressive models (Meloun and Militký 1996). We may
Regression technique can be best suited for predications in agriculture. In this paper regression analysis models the relationship between factors like plant height and tiller number which are independent variables with the yields of a crop (like rice plant) which is a dependent variable. In data mining independent variables are attributes already known and response variables are what we want to predict. Data analytics here involves regression analysis with more than one independent variable which is called multiple regression analysis. When all independent variable are assumed to affect the dependent variable in a linear proportion and independently of one another, the procedure is called multiple linear regression analysis. The simple liner regression and correlation analysis has one major limitation. That is, that it is applicable only to cases with one independent variable. There is a corresponding increase in need for use of regression procedures that can simultaneously handle several independent variables. A multiple linear regression is said to be operating if the relationship of the dependent variable Y to the k independent variables X 1 , X 2 , X k can be expressed as Y= α + β+ β 1 X 1 + β 2 X 2 ...+
sources (e.g. saline/brackish waters, desalinated water, and treated wastewater). All these prob- lems will become more pronounced in the years to come, as society enters an era of increasingly complex paths towards the global economy. In this context, engineers and decision-makers need to systematically review planning principles, design criteria, operating rules, contingency plans and management policies for new infra-structures. In relation to these issues and based on availa- ble information, this report gives an overview of current and future (time horizon 2025) irrigation and food production development around the world. Moreover, the paper analyses the results of the most recent and advanced General Circulation Models for assessing the hydrological impacts of climate variability on crop requirements, water availability and the planning and design process of irrigation systems. Finally, a five-step planning and design procedure is proposed that is able to integrate, within the development process, the hydrological consequences of climate change. For researchers interested in irrigation and drainage and in crop production under changing climate conditions, references have been included, under developments in irrigation section on Page 3. Many climate action plans developed by few cities, states and various countries are cited for policy makers to follow or to make a note off. Few citations are also included in the end to educate every one of us, who are not familiar with the scientific work of our colleagues, related to global warm- ing. The colleagues are from different areas, physics, mathematics, agricultural engineering, crop scientists and policy makers in United Nations. Most of the citation links do open, when you click on them. If it does not, copy and paste the link on any web browsers.
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Globally, the proportion of freshwater consumption by agri- culture from rainfall as well as surface and groundwater re- sources is large (9087 km 3 yr −1 ) (Hoekstra and Mekonnen, 2012). It is projected that water demand will increase in the future, in particular by irrigation agriculture, in order to sup- port the increasing world population with food (Foley et al., 2011; De Fraiture and Wichelns, 2010; Hanjra and Qureshi, 2010; Wada and Bierkens, 2014). Therefore, strategies based on improved irrigation methods and local adaptions of man- agement practices are likely to be implemented to anticipate this trend. Such strategies are often developed using decision support systems that are informed by mathematical models. For example, irrigation management has been optimized by modelling and measurements for crops grown in Central Asia (Pereira et al., 2009) and for irrigated cotton in the High Plains region of Texas (Howell et al., 2004). Others have in- vestigated water use efficiency (Wang et al., 2001) or crop water productivity (Liu et al., 2007) by modelling experi- ments for irrigated crops grown in China.
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The experience obtained in these projects indicate that additional conclusions can be extracted if the methodology is analysed from the perspective of the end user (V. Bodas, personal communication). Easy access to timely information is crucial. Direct access by farmers in real-time to the images in the way of the usual RGB colour combination is very useful. These RGB/NDVI images enable farmers to gain confidence in identifying some details in the images they have observed directly in their fields, such as sprinkler failure and non-uniformity water distribution effects, among others. These conclusions are shared by the different web-GIS analyzed, and the basic information provided by each system is similar: vegetation indices, color composites, and core biophysical parameters derived from satellite data and related with the water use, like crop coefficients. All of the systems take into account the necessity of the spatio-temporal analysis, and the user can visualize the images and query the information for different dates or time periods. An interesting option in all systems is the capability to display the location of the user or device in the maps. This geolocation, with the reference of the most recent satellite images, can be used to identify areas of interest in the field, like zones with unusual crop development. An additional point of general agreement is that weekly is the best compromise of timing for using and receiving the information about plant status and CWR. However, the information about CWR or NIWR must be provided with sufficient anticipation, because of the time required to modify the irrigation scheduling, adapted to the power supply rates, water availability, irrigation system, precipitation probability and farmer´s availability. Usually the farmers are willing to adopt these techniques are familiar with point ground moisture sensors in such a way that they are able to check with their own knowledge the reliability of RS
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