ESTIMATING CORN YIELD RESPONSE MODELS TO PREDICT IMPACTS OF CLIMATE CHANGE

Projections of the impacts of climate change on agriculture require flexible and accurate yield response models. Typically, estimated yield response models have used fixed calendar intervals to measure weather variables and omitted observations on solar radiation, an essential determinant of crop yield. A corn yield response model for Illinois crop reporting districts is estimated using field data. Weather variables are time to crop growth stages to allow use of the model if climate change shifts dates of the crop growing season. Solar radiation is included. Results show this model is superior to conventionally specified models in explaining yield variation in Illinois corn.Crop Production/Industries,

A REVIEW AND EVALUATION OF WEATHER-CROP YIELD MODELS

The purpose of this paper is the relatively limited one of reviewing the literature for models which develop specific relationships between climatic variables and crop yields. Following a review of recent weather-crop yield modeling efforts we evaluate these models and suggest some conceptual models and data base improvements if we are to adequately project the impacts on crop production of expected future climatic change. Our review and evaluation centers on weather-crop yield models applicable to the central grain belt of the U.S., mainly the Corn Belt and Great Plains production regions.Crop Production/Industries,

Impact of climate change using CRAFT: a case study for West Africa

The CGIAR research program on Climate Change, Agriculture and Food Security Program’s (CCAFS) Regional Agricultural Forecasting Toolbox (CRAFT) is a framework for multi-scale spatial gridded simulations using an ensemble of crop models. The toolbox facilitates studies on the potential impact of climate change on crop production for a region in addition to other capabilities such as the regional in-season yield forecasting and risk assessment. CRAFT can be used to generate and conduct multiple simulation scenarios, maps, and interactive visualizations using a crop engine that can run the crop simulation models DSSAT, APSIM, and SARRA-H, in concert with the Climate Predictability Tool (CPT) for probabilistic seasonal climate forecasts

Simulating Root Density Dynamics and Nitrogen Uptake -Field Trials and Root Model Approach in Denmark

Plant soil and atmosphere models are commonly used to predict crop yield and associated environmental consequences. Such models often include complex modelling of water movement, soil organic matter turnover and above ground plant growth. However, the root modelling in these models is often very simple, partly due to a limited access to experimental data. Here we propose a root model developed to describe root growth, root density and nitrogen uptake. The model focuses on annual crops, and attempts to model root growth of different crop species and row crops and its significance for nitrogen uptake from different parts of the soil volume

Research in the application of spectral data to crop identification and assessment, volume 2

The development of spectrometry crop development stage models is discussed with emphasis on models for corn and soybeans. One photothermal and four thermal meteorological models are evaluated. Spectral data were investigated as a source of information for crop yield models. Intercepted solar radiation and soil productivity are identified as factors related to yield which can be estimated from spectral data. Several techniques for machine classification of remotely sensed data for crop inventory were evaluated. Early season estimation, training procedures, the relationship of scene characteristics to classification performance, and full frame classification methods were studied. The optimal level for combining area and yield estimates of corn and soybeans is assessed utilizing current technology: digital analysis of LANDSAT MSS data on sample segments to provide area estimates and regression models to provide yield estimates

Crop conditional Convolutional Neural Networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions

Convolutional Neural Networks (CNN) have demonstrated their capabilities on the agronomical field, especially for plant visual symptoms assessment. As these models grow both in the number of training images and in the number of supported crops and diseases, there exist the dichotomy of (1) generating smaller models for specific crop or, (2) to generate a unique multi-crop model in a much more complex task (especially at early disease stages) but with the benefit of the entire multiple crop image dataset variability to enrich image feature description learning. In this work we first introduce a challenging dataset of more than one hundred-thousand images taken by cell phone in real field wild conditions. This dataset contains almost equally distributed disease stages of seventeen diseases and five crops (wheat, barley, corn, rice and rape-seed) where several diseases can be present on the same picture. When applying existing state of the art deep neural network methods to validate the two hypothesised approaches, we obtained a balanced accuracy (BAC=0.92) when generating the smaller crop specific models and a balanced accuracy (BAC=0.93) when generating a single multi-crop model. In this work, we propose three different CNN architectures that incorporate contextual non-image meta-data such as crop information onto an image based Convolutional Neural Network. This combines the advantages of simultaneously learning from the entire multi-crop dataset while reducing the complexity of the disease classification tasks. The crop-conditional plant disease classification network that incorporates the contextual information by concatenation at the embedding vector level obtains a balanced accuracy of 0.98 improving all previous methods and removing 71% of the miss-classifications of the former methods