Agricultural Crop Forecasting for Large Geographical Areas

Young, Linda J.

doi:10.1146/annurev-statistics-030718-105002

Cited by 15 publications

(4 citation statements)

References 53 publications

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“…Although land surface models coupled with crop models (such as LPJmL, ORCHIDEE‐CROP, see Table S3 in Supporting Information S1) generally exhibit superior performance, these models require high temporal resolution atmospheric forcing, which is challenging to obtain for future scenarios. Nevertheless, WOFOST/PCSE has been used widespread for the past a quarter of a century in crop modeling projects across Europe, Africa, and China (Ceglar et al., 2019; De Wit et al., 2019; Gaso et al., 2021; Gilardelli et al., 2018; Todorovic et al., 2009; Young, 2019). For example, it is an integral part of the operational protocols of the EU Monitoring Agricultural ResourceS (MARS) system and the Growth Simulating and Monitoring System in China (CGMS‐China), both aimed at agricultural monitoring and crop yield forecasting.…”

Section: Methodsmentioning

confidence: 99%

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

Xu,

Liang,

Wei

et al. 2024

Earth's Future

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Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally.

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Section: Methodsmentioning

confidence: 99%

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

Xu,

Liang,

Wei

et al. 2024

Earth's Future

View full text Add to dashboard Cite

show abstract

“…On the other hand, time series analysis has been applied in order to predict the production and the price of crops and vegetables. The aim was to identify a time series function, which might identify patterns and seasonality in specific vegetables, as well as explore supply and demand variables ( Bagis et al., 2012 ) ( Jha and Sinha, 2013 ) ( Young, 2019 ). In addition, many researchers proposed a methodology that uses Average Pearson Correlation (APC) and Coefficient of Variance (CV) to determine indications that reveal crop price fluctuation ( Pereira et al., 2021 ).…”

Section: Related Workmentioning

confidence: 99%

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

et al. 2023

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Agriculture is the most critical sector for food supply on the earth, and it is also responsible for supplying raw materials for other industrial productions. Currently, the growth in agricultural production is not sufficient to keep up with the growing population, which may result in a food shortfall for the world’s inhabitants. As a result, increasing food production is crucial for developing nations with limited land and resources. It is essential to select a suitable crop for a specific region to increase its production rate. Effective crop production forecasting in that area based on historical data, including environmental and cultivation areas, and crop production amount, is required. However, the data for such forecasting are not publicly available. As such, in this paper, we take a case study of a developing country, Bangladesh, whose economy relies on agriculture. We first gather and preprocess the data from the relevant research institutions of Bangladesh and then propose an ensemble machine learning approach, called K-nearest Neighbor Random Forest Ridge Regression (KRR), to effectively predict the production of the major crops (three different kinds of rice, potato, and wheat). KRR is designed after investigating five existing traditional machine learning (Support Vector Regression, Naïve Bayes, and Ridge Regression) and ensemble learning (Random Forest and CatBoost) algorithms. We consider four classical evaluation metrics, i.e., mean absolute error, mean square error (MSE), root MSE, and R2, to evaluate the performance of the proposed KRR over the other machine learning models. It shows 0.009 MSE, 99% R2 for Aus; 0.92 MSE, 90% R2 for Aman; 0.246 MSE, 99% R2 for Boro; 0.062 MSE, 99% R2 for wheat; and 0.016 MSE, 99% R2 for potato production prediction. The Diebold–Mariano test is conducted to check the robustness of the proposed ensemble model, KRR. In most cases, it shows 1% and 5% significance compared to the benchmark ML models. Lastly, we design a recommender system that suggests suitable crops for a specific land area for cultivation in the next season. We believe that the proposed paradigm will help the farmers and personnel in the agricultural sector leverage proper crop cultivation and production.

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“…Within season, crop yield forecasts are valuable for supporting stakeholder decision making including individual farmers, governments and non-profit international organizations [1][2][3]. The crop yield forecasts are typically based on field surveys and measurements.…”

Section: Introductionmentioning

confidence: 99%

Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

et al. 2021

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Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.

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Agricultural Crop Forecasting for Large Geographical Areas

Cited by 15 publications

References 53 publications

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

Assessing Climate Change Impacts on Crop Yields and Exploring Adaptation Strategies in Northeast China

Ensemble machine learning-based recommendation system for effective prediction of suitable agricultural crop cultivation

Analogy-Based Crop Yield Forecasts Based on Temporal Similarity of Leaf Area Index

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