Department of Mathematics and Statistics, University of Guelph, Guelph, N1G 2W1, Canada.
Sci Rep. 2023 Apr 26;13(1):6823. doi: 10.1038/s41598-023-33840-6.
Land suitability models for Canada are currently based on single-crop inventories and expert opinion. We present a data-driven multi-layer perceptron that simultaneously predicts the land suitability of several crops in Canada, including barley, peas, spring wheat, canola, oats, and soy. Available crop yields from 2013-2020 are downscaled to the farm level by masking the district level crop yield data to focus only on areas where crops are cultivated and leveraging soil-climate-landscape variables obtained from Google Earth Engine for crop yield prediction. This new semi-supervised learning approach can accommodate data from different spatial resolutions and enables training with unlabelled data. The incorporation of a crop indicator function further allows for the training of a multi-crop model that can capture the interdependences and correlations between various crops, thereby leading to more accurate predictions. Through k-fold cross-validation, we show that compared to the single crop models, our multi-crop model could produce up to a 2.82 fold reduction in mean absolute error for any particular crop. We found that barley, oats, and mixed grains were more tolerant to soil-climate-landscape variations and could be grown in many regions of Canada, while non-grain crops were more sensitive to environmental factors. Predicted crop suitability was associated with a region's growing season length, which supports climate change projections that regions of northern Canada will become more suitable for agricultural use. The proposed multi-crop model could facilitate assessment of the suitability of northern lands for crop cultivation and be incorporated into cost-benefit analyses.
加拿大的土地适宜性模型目前基于单一作物清单和专家意见。我们提出了一种数据驱动的多层感知器，可以同时预测加拿大几种作物的土地适宜性，包括大麦、豌豆、春小麦、油菜籽、燕麦和大豆。利用谷歌地球引擎获取的土壤-气候-景观变量，将 2013-2020 年的作物产量从地区层面下推到农场层面，只关注作物种植的区域，并屏蔽地区层面的作物产量数据。这种新的半监督学习方法可以适应不同空间分辨率的数据，并可以使用未标记的数据进行训练。作物指标函数的加入进一步允许训练多作物模型，该模型可以捕捉各种作物之间的相互依存和相关性，从而实现更准确的预测。通过 k 折交叉验证，我们表明与单一作物模型相比，我们的多作物模型可以将任何特定作物的平均绝对误差降低多达 2.82 倍。我们发现大麦、燕麦和混合谷物对土壤-气候-景观变化的耐受性更强，可以在加拿大的许多地区种植，而非谷物作物对环境因素更敏感。预测的作物适宜性与一个地区的生长季节长度有关，这支持了加拿大北部地区将变得更适合农业利用的气候变化预测。所提出的多作物模型可以促进对北部土地种植作物的适宜性的评估，并纳入成本效益分析。
