Modeling and Forecasting of Agricultural Commodity Production under Changing Climatic Condition: A Review

India is a country with an agrarian economy in which majority of its population rely on agriculture directly as their source of livelihoof. Climate has a very significant role in agricultural production. It predominantly influences growth of the crop, development of the crop and eventually crop yield. Climate also significantly influences the outbreak of disease and pest; it affects the requirement of water by the crop. Possible changes in weather factors, like precipitation, temperature and CO2 concentration are expected to have a significant impact on crop growth. If farmers are able to predict the weather activities and are aware of the effect of these activities on crop production, then it will be beneficial to them as a feasible plan can be devised synchronizing the crop production activities as per changes in the climatic conditions. In view of tackling the aforementioned problem, this article describes various statistical techniques that can play a crucial role in forecasting production of agricultural commodities changing climatic conditions.

CDC Hickie yellow field pea

CDC Hickie, a yellow cotyledon field pea (Pisum sativum L.) cultivar, was released in 2021 by the Crop Development Centre, University of Saskatchewan for distribution to Select seed growers through the Variety Release Committee of the Saskatchewan Pulse Growers. CDC Hickie has good lodging resistance, medium time to maturity, medium-sized, round seeds, mean seed protein concentration of 24.4%, and good yielding ability. It is resistant to powdery mildew and moderately susceptible to mycosphaerella blight and Fusarium root rot. CDC Hickie is adapted to the field pea growing regions of western Canada.

CDC Lewochko yellow field pea

CDC Lewochko, a yellow cotyledon field pea (Pisum sativum L.) cultivar, was released in 2018 by the Crop Development Centre, University of Saskatchewan for distribution to Select seed growers through the Variety Release Committee of the Saskatchewan Pulse Growers. CDC Lewochko has good lodging resistance, medium time to maturity, medium-sized, round seeds, mean seed protein concentration of 21.0%, and good yielding ability. It is resistant to powdery mildew and moderately susceptible to mycosphaerella blight and Fusarium root rot. CDC Lewochko is adapted to the field pea growing regions of western Canada.

CDC Tollefson yellow field pea

CDC Tollefson, a yellow cotyledon field pea (Pisum sativum L.) cultivar, was released in 2021 by the Crop Development Centre, University of Saskatchewan for distribution to Select seed growers through the Variety Release Committee of the Saskatchewan Pulse Growers. CDC Tollefson has good lodging resistance, medium time to maturity, medium-sized, round seeds, mean seed protein concentration of 23.9%, and good yielding ability. It is resistant to powdery mildew and moderately susceptible to mycosphaerella blight and Fusarium root rot. CDC Tollefson is adapted to the field pea growing regions of western Canada.

A Crop Modelling Strategy to Improve Cacao Quality and Productivity

Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/−2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51.

An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development

Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest.

Effects of diversifying the planting period of maize on herbivorous and their natural enemies

Knowledge of the specific insect densities during crop development is necessary to perform appropriate measures for the control of insect pests and to minimize yield losses. In a previous study, both spatial and temporal approaches were adopted to analyse the influence of landscape structure and field variables on herbivore and predatory insects on maize. Both types of variables influenced insect abundance, but the highest effect was found with maize phenology. Given that the field planting date could modulate the influence produced by the structure of the landscape on herbivores and predatory insects, analyses of population dynamics must be performed at both the local and landscape levels. The anterior prompted us to study these aspects in the two common planting periods (early and late) in northern Spain. The present study tests the hypothesis that the period of maize planting could have a higher effect than phenology or interannual variation on the abundance of natural enemies and herbivores on maize. Our results showed that only the abundances of other herbivore thrips and Syrphidae were significantly different between the two planting periods. Moreover, we found significant effects of planting period when we performed yearly analysis in 2015 for Coccinellidae and Chrysopidae and in 2016 and 2017 for Aeolothrips sp. Most of the taxa had abundance peaks in earlier growth stages, which are related to pollination (before or during), while only Stethorus punctillum and Syrphidae increased later in the season. Furthermore, Frankliniella occidentalis, aphids, Syrphidae and Coccinellidae registered higher abundances in fields sown in the late planting period than in the rest of the insect species. The results of the present study highlight the effects of sowing dates on insect dynamics in maize.

Bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii crops evaluated by metagenomics

Soil quality is usually determined by its physical-chemical characteristics without taking into account the bacterial communities that play a fundamental role in the chemical decomposition of plant nutrients. In this context, the objective of the study was to evaluate bacterial diversity in high Andean grassland soils disturbed with Lepidium meyenii cultivation under different gradients of use (first, second and third use) and crop development (pre-sowing, hypocotyl development and post-harvest). The sampling was carried out in the Bombón plateau in the central Andes of Peru, during the rainy and low water seasons, by the systematic method based on a specific pattern assigned in a geometric rectangular shape at a depth of 0 - 20 cm. The characterization of the bacterial communities was carried out through the metagenomic sequencing of the 16S rRNA. 376 families of bacteria were reported, of which it was determined that there was a significant change in bacterial composition and distribution in relation to use pressure. There were no major changes due to the development of Lepidium meyenii. The families most sensitive to use pressure and soil poverty indicators were Verrucomicrobiaceae, Acidobacteraceae and Aakkermansiaceae.

PHYSIOLOGICAL PERFORMANCE OF COMMON BEANS SUBMITTED TO DEFICIT IRRIGATION AND BIOSTIMULANTS

The common bean is a sensitive plant to the effects of water deficit and physio-biochemical alterations which influence the yield are observed. Vegetal biostimulants are inputs which present potential to mitigate the effects of water deficit on crop development. This paper aimed to evaluate the physiological and biochemical impact of water deficit on common bean and the potential of applying biostimulants as a mechanism to tolerate the stress. The assay was conducted in an agricultural greenhouse in Botucatu-Brazil, the pots were disposed in a split-plot design in randomized blocks, with four replications. The treatments in the plots corresponded to the water content tension, control (-10 kPa) and water deficit (-40 kPa), in the subplots the biostimulants treatments (B1- control; B2- Bacillus amyloliquefaciens BV; B3- Bacillus amyloliquefaciens BV 03 + algae extract Ascophyllum nodosum). The analyzed variables were: leaf pigments, gas exchanges, total soluble proteins, L-proline, specific activity of the enzyme superoxide dismutase, shoot dry matter and crop yield. The water deficit affected negatively all parameters evaluated and the biostimulants in the tested form, did not show efficiency in helping the plants to tolerate stress due to drought. We suggest new studies to prove the efficiency of biostimulants for field applications.