Analysis of growth dynamics of Mediterranean bioenergy crops

In spite of the rapidly growing bioenergy production worldwide, there is lack of field experience and experimental data on the cultivation of bioenergy crops. This study aims to advance crop management operations and modelling studies by providing essential information on phenology, agronomy and crop physiology of three Mediterranean bioenergy crops: Helianthus annuus (sunflower), Hibiscus cannabinus (kenaf) and Cynara cardunculus (cynara). These crops cover a wide range of bio-industrial applications and fit into different cropping strategies. For these crops, we identified the most important knowledge gaps and performed a series of field experiments to fill some of those, particularly for cynara. Information on phenology and seed yield potential for cynara was missing mainly due to its complex inflorescence structure. This thesis codifies and describes cynara’s phenological growth stages according to the universal BBCH coding system. This scale can be used by everyone involved in the production of this crop under all circumstances. In addition, we present a robust allometric model for estimating seed yield under diverse management and environmental conditions. Inputs to the model are two easily quantifiable inflorescence traits: total weight and number of seed-bearing heads per unit area. Additionally, this thesis investigates factors at leaf, canopy and crop level that determine biomass production for all tested crops and provides key parameters for crop growth modelling. Leaf photosynthesis and respiration rates in response to light, temperature and leaf nitrogen were quantified. Based on such data, a biochemical model for C3 leaf photosynthesis and an empirical model for respiration were parameterized and validated. Then, to upscale these rates from the leaf to the canopy level, light- and nitrogen extinction coefficients over time and in response to water availability were determined in detail. It was shown that the light extinction coefficient changes under water stress conditions and time of year, while leaf nitrogen only shows a strong vertical distribution within crop canopy during the mid-season. Relevant agronomic data, such as biomass production over time and leaf area index in response to management practices, are also presented for the three crops. This thesis contributes to the general objective of gaining more insight into bioenergy production from crop species. The findings can help farmers, researchers and modellers to better evaluate agricultural land uses and to improve biomass quantity and quality. Among the studied species, the perennial cynara shows the greatest potential for energy production in the Mediterranean region because a significant part of the production is achieved in the winter–spring period relying on natural rainfall. Key words: cynara, kenaf, sunflower, phenology, agronomy, crop physiology, modelling, biomass production, crop growth, growth stages, BBCH code, seed yield, oil/seed ratio, leaf area index, leaf nitrogen, light and nitrogen extinction coefficients, photosynthesis, respiration, respiration acclimation, bioenergy, Greece, Mediterranean region. </p

Influence of Drought on Corn and Soybean

Water is extremely important for crop production. When water becomes limiting to the plant it is important to understand how plants use water. We often hear the term evapotranspiration (ET) in relation to plant water demand. ET is a combination of soil water evaporation (E) and water used by the plant during transpiration (T). Soil evaporation is the major loss of water surface and typically is higher after rain and under high temperature conditions

Yield Gap Analysis: What Limited Iowa Corn and Soybean Yields in 2015

To evaluate how good corn and soybean yields were in 2015, farmers and agronomists compare their yields to those obtained in previous years. To answer why yields were higher or lower than past years, they develop hypotheses to explain factors that limited yields based on their own experiences, anecdotal evidence from neighbors, knowledge of crop growth and development, and weather patterns. As a next step to the in-season Yield Forecast Project, we can provide an alternative analysis and a yield gap analysis of the 2015 growing season by using the explanatory power that a cropping systems model offers. Our analysis is focused on corn and soybean cropping systems used in the Yield Forecast Project (more information on the cropping systems and the Yield Forecast Project can be found in the June 17thICM News article). In total, we evaluated eight cropping systems; two locations, two crops, two planting dates

Corn Water Use and Evapotranspiration

Crop water use (transpiration) during the growing season is a major factor in attaining high yield potential. Soil water loss (evaporation) and crop water loss (transpiration) occur simultaneously; making predictions of evapotranspiration complex. Actual evapotranspiration values vary greatly from day to day (0.04 to 0.40 inches/day) because of the following factors: Soil: residue cover, soil texture, soil moisture in the profile Crop: crop type, growth stage, cultivar Climate: radiation, temperature, relative humidity, wind spee

How Fast and Deep do Corn Roots Grow in Iowa?

Corn roots grow rapidly starting at the 4th-leaf stage and continue throughout vegetative development. This typically occurs from June to early July. Several factors affect root growth, but temperature and soil moisture are the most relevant factors in the absence of soil constraints. Well-developed, deep root systems are essential to support water and nutrient uptake and thus high yield potential. Hot and dry weather results in a depletion of moisture in the top 6-inch soil layer. This occurred in June of 2016 and also during the first two weeks of June 2017. Crop stress was evident in light soils or where root development was restricted. Should you be concerned about this? Maybe, maybe not. It is known that plant roots cannot grow in dry or saturated soil conditions. However, at this time it is unlikely that water is limiting root growth below a 6-inch soil depth

A CNN-RNN Framework for Crop Yield Prediction

Crop yield prediction is extremely challenging due to its dependence on multiple factors such as crop genotype, environmental factors, management practices, and their interactions. This paper presents a deep learning framework using convolutional neural networks (CNN) and recurrent neural networks (RNN) for crop yield prediction based on environmental data and management practices. The proposed CNN-RNN model, along with other popular methods such as random forest (RF), deep fully-connected neural networks (DFNN), and LASSO, was used to forecast corn and soybean yield across the entire Corn Belt (including 13 states) in the United States for years 2016, 2017, and 2018 using historical data. The new model achieved a root-mean-square-error (RMSE) 9% and 8% of their respective average yields, substantially outperforming all other methods that were tested. The CNN-RNN have three salient features that make it a potentially useful method for other crop yield prediction studies. (1) The CNN-RNN model was designed to capture the time dependencies of environmental factors and the genetic improvement of seeds over time without having their genotype information. (2) The model demonstrated the capability to generalize the yield prediction to untested environments without significant drop in the prediction accuracy. (3) Coupled with the backpropagation method, the model could reveal the extent to which weather conditions, accuracy of weather predictions, soil conditions, and management practices were able to explain the variation in the crop yields.Comment: 26 Pages, 14 Figure