Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

An incorporative framework is proposed in this study for crop yield modelling and forecasting. It is a complementary approach to traditional time series analysis on modelling and forecasting by treating crop yield and associated factors as a non-temporal collection. Statistics are used to identify the highly related factor(s) among many associates to crop yield and then play a key role in data cleaning and a supporting role in data expansion, if necessary, for neural network training and testing. Wheat yield and associated plantation area, rainfall and temperature in Queensland of Australia over 100 years are used to test this incorporative approach. The results show that welltrained multilayer perceptron models can simulate the wheat production through given plantation areas with a mean absolute error (MAE) of *2%, whereas the thirdorder polynomial correlation returns an MAE of *20%. However, statistical analysis plays a key role in identifying the most related factor, detecting outliers, determining the general trend of wheat yield with respect to plantation area and supporting data expansion for neural network training and testing. The combination of these two methods provides both meaningful qualitative and accurate quantitative data analysis and forecasting. This incorporative approach can also be useful in data modelling and forecasting in other applications due to its generic nature.

The indoor air temperature of greenhouse is an important parameter in environmental monitoring. Existing research on it has many, and the mathematical models have been made. But the obtained models do not apply to the northern areas features of dry and cold. On the basis of previous studies, the indoor air temperature model of greenhouse will be re-modeling by using the regression analysis method in this paper. Analysis each convection heat transfer coefficient of the model and re-established them. In the case of known the outdoor weather conditions, the solution of the indoor temperature will be implemented by using the computer programming. And the data of a period is selected randomly from the observational data to validate the model is feasible and applied.

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