Detecting Sugarcane Crop Yield using Decision Tree Classifier in the District of Muzaffarnagar

The district of Muzaffarnagar is the highest sugarcane producing district in Uttar Pradesh and therefore is an important industrial district as well. The district is part of Western UP and it shares the problems of the sugar industry elsewhere in the state: unpredictable demands and crop failures. In this context, predicting sugarcane demand and informing its production can turn to be just the key to solve some of the problems the industry faces. The existing crop forecasting method for the cultivation of sugarcane used in UP relies, to a large degree, on subjective details, centred on the expertise of engineers in the sugar and alcohol field and on information on input demand in the supply chain. The measurement of the utility of the sample detection using NDVI images from the SPOT sensor used in the sensor's determination over the ECMWF model was possible to infer the official productivity data reported in the previously selected municipalities and harvest. Significant features of the municipal productivity of a given village is listed in a decision tree, and out of the combinations of attributes the corresponding municipal productivity is rated as "Normal" on the average urban productivity scale. Using data from the NDVI time-series between 2013 to 2020, we can discern the three classes of productivity in the meanwhile. Findings indicate that productivity in January ranked as less than mean, mean, and more than mean. The findings were more successful for the class Vegetation, the participants of which were permitted to conclude about the pattern of the average federal productivity prior to.

Evaluating the 2019 NARO-APCC Joint Crop Forecasting Service yield forecasts for Northern Hemisphere countries

Forecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The NARO-APCC Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and one year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the United States Department of Agriculture (USDA). Forecasts for maize, wheat, soybean and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the US, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skilful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

Global within-season yield anomaly prediction for major crops derived using seasonal forecasts of large-scale climate indices and regional temperature and precipitation

Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-year (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

Floral Meristem Development in Cranberry Apical Buds during Winter Rest and Its Implication on Yield Prediction

The formation and development of floral meristems is key to fruit production. However, limited information regarding the development of floral buds during the dormant period of cranberry (Vaccinium macrocarpon) constrains the ability to forecast yield early and accurately. The objectives of this study were to characterize the development of floral meristems from fall to spring and to evaluate the number of floral meristems formed across different bud sizes and upright types, as well as their contribution to the fruit production of the next year. Apical buds of different sizes on vegetative and fruiting uprights were tagged and collected periodically from fall to spring for histological study. An extra set of tagged buds was left in the field to evaluate their flower and fruit production. Five stages of floral development were identified based on the concentric differentiation of organ primordia. Large buds from vegetative uprights developed earlier, had a higher number of floral meristems, and became fruiting uprights; they had the highest number of flowers and fruit. Buds from fruiting uprights had the lowest number of floral meristems and delayed development; subsequently, they had the lowest number of fruit per upright. Our results provide evidence of active floral meristem differentiation during fall and winter, as well as differences in the timing and development stage according to bud size. In addition, our study shows that upright types and bud sizes influence the fruit production of the following year; therefore, they should be considered in cranberry crop forecasting models.

Agricultural Crop Forecasting for Large Geographical Areas

Crop forecasting is important to national and international trade and food security. Although sample surveys continue to have a role in many national crop forecasting programs, the increasing challenges of list frame undercoverage, declining response rates, increasing response burden, and increasing costs are leading government agencies to replace some or all of survey data with data from other sources. This article reviews the primary approaches currently being used to produce official statistics, including surveys, remote sensing, and the integration of these with meteorological, administrative, or other data. The research opportunities for improving current methods of forecasting crop yield and quantifying the uncertainty associated with the prediction are highlighted.