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.

Functional–Structural Plant Models Mission in Advancing Crop Science: Opportunities and Prospects

Functional–structural plant models (FSPMs) have been evolving for over 2 decades and their future development, to some extent, depends on the value of potential applications in crop science. To date, stabilizing crop production by identifying valuable traits for novel cultivars adapted to adverse environments is topical in crop science. Thus, this study will examine how FSPMs are able to address new challenges in crop science for sustainable crop production. FSPMs developed to simulate organogenesis, morphogenesis, and physiological activities under various environments and are amenable to downscale to the tissue, cellular, and molecular level or upscale to the whole plant and ecological level. In a modeling framework with independent and interactive modules, advanced algorithms provide morphophysiological details at various scales. FSPMs are shown to be able to: (i) provide crop ideotypes efficiently for optimizing the resource distribution and use for greater productivity and less disease risk, (ii) guide molecular design breeding via linking molecular basis to plant phenotypes as well as enrich crop models with an additional architectural dimension to assist breeding, and (iii) interact with plant phenotyping for molecular breeding in embracing three-dimensional (3D) architectural traits. This study illustrates that FSPMs have great prospects in speeding up precision breeding for specific environments due to the capacity for guiding and integrating ideotypes, phenotyping, molecular design, and linking molecular basis to target phenotypes. Consequently, the promising great applications of FSPMs in crop science will, in turn, accelerate their evolution and vice versa.

From Kolisko to nowadays: progresses and discoveries in agro-homeopathy.

The use of ultra-diluted preparations method in agriculture was introduced with agro-homeopathy, which allows to influence biological processes of plants by either accelerating or delaying growth. Moreover, it can contribute to the control of plagues and diseases, directly promoting an increase of the yield and an improvement of product qualitative traits. Since the pioneering works of Kolisko on wheat germination [1] and Junker on growth of microorganisms (paramecium, yeast, fungi) [2], in the last 30 years work has flourished from independent researchers from worldwide (Americas, Europe and Australasia). The international research works on agro-homeopathy can be conceptually divided in two main groups: effects of ultra-diluted preparations on crop growth and applicability for crop disease control. The first type of investigations usually are carried out on both healthy organisms for determining the growth stimulation of treatments and on abiotically stressed plants (i.e. heavy metal over-exposition, salt excess, water and nutrients deficiency) for determining the re-growth stimulation of ultra-dilutions [3,4]. The second type of investigations are usually performed on artificially diseased organisms (i.e. fungal and viral pathogens or nematode infection), which may react more markedly to homeopathic treatments than healthy ones [5]. Unfortunately, on the basis of the extensive critical review of published papers, there is little firm evidence to support the reliability of the reported results. Except for a limited number of publications, the most common drawbacks of agro-homeopathy researches are the poor experimental methodology and the inadequate statistical analysis. Moreover, since there is no agricultural homeopathic pharmacopoeia, much work is required to find suitable remedies, potencies and dose levels. Considering the criticism on the practical applicability of ultra-diluted preparations, in order to be accepted as a valid part of agricultural practices a well structured research strategy for agro-homeopathy is needed. This is often hampered by methodological problems as well as by the general underinvestment on the academic and nonacademic research structures. Fundamental researches based on collaborative approaches (i.e. ring tests on selected crop models) and on common experimental protocols (i.e. statistical robustness) are the keys for determining the worldwide acceptability of agro-homeopathy as a sustainable agro-technique. Statement of conflict of interest Authors declare there is no conflict of interest. Statement of financial support Authors declare that this study received no funding. Bibliography 1. Kolisko L. Physiologischer und physikalischer Nachweis der Wirksamkeit kleinster Entitäten. 1923; Stuttgart, Verlag Der Kommende Tag AG. 2. Junker H.. Die Wirkung extremer Potenzverdünnungen auf Organismen. Pflugers Arch ges Phys 1928; 219B, 5/6, 647-672. 3. Jäger T, Scherr C, Shah D, Majewsky V, Betti L, Trebbi G, Bonamin L, Simões-Wüst AP, Wolf U, Simon M, Heusser P, Baumgartner S. Use of homeopathic preparations in experimental studies with abiotically stressed plants. Homeopathy 2011; 100: 275-287 4. Majewsky V, Heuwieser, Shah D, Scherr C, Jaeger Tim, Betti L, Trebbi G, Bonamin L, Klocke P, Baumpartner S. Use of homeopathic preparations in experimental studies with healthy plants. Homeopathy 2009; 98: 228-243. 5. Betti L, Trebbi G, Majewsky V, Scherr C, Shah-Rossi D, Jäger T, Baumgartner S. Use of homeopathic preparations in phytopathological models and in field trials: a critical review. Homeopathy 2009; 98: 244-266.

Simulation of Staple Crop Yields for Determination of Regional Impacts of Climate Change: A Case Study in Chonnam Province, Republic of Korea

This study sought to simulate regional variation in staple crop yields in Chonnam Province, Republic of Korea (ROK), in future environments under climate change based on the calibration of crop models in the Decision Support System for Agricultural Technology Transfer 4.6 package. We reproduced multiple-year yield data for paddy rice (2013–2018), barley (2000–2018), and soybean (2004–2018) grown in experimental fields at Naju, Chonnam Province, using the CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system developed using the crop models was then applied to simulate the regional impacts of climate change on the staple crops according to the Representative Concentration Pathway 4.5 and 8.5 scenarios. Simulated crop yields agreed with the corresponding measured crop yields, with root means square deviations of 0.31 ton ha−1 for paddy rice, 0.29 ton ha−1 for barley, and 0.27 ton ha−1 for soybean. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in the impact of climate change on staple crop yield. The CERES and CROPGRO models seem to reproduce the effects of climate change on region-wide staple crop production in a monsoonal climate system. Added advancements of the GCSM system could facilitate interpretations of future food resource insecurity and establish a sustainable adaption strategy.

Simulation of Crop Yields Grown under Agro-Photovoltaic Panels: A Case Study in Chonnam Province, South Korea

Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models in the decision support system for agricultural technology (DSSAT) 4.6 package. We reproduced yield data of paddy rice, barley, and soybean grown in AVP experimental fields in Bosung and Naju, Chonnam Province, South Korea, using CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system, developed using the crop models, was then applied to simulate the regional variations in crop yield according to solar radiation reduction scenarios. Simulated crop yields agreed with the corresponding measured crop yields with root mean squared errors of 0.29-ton ha−1 for paddy rice, 0.46-ton ha−1 for barley, and 0.31-ton ha−1 for soybean, showing no significant differences according to paired sample t-tests. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in crop yields due to the solar radiation reduction regimes. An additional advancement in the GCSM design could help prepare a sustainable adaption strategy and understand future food supply insecurity.

Soybean-BioCro: A semi-mechanistic model of soybean growth

Soybean is a major global source of protein and oil. Understanding how soybean crops will respond to the changing climate and identifying the responsible molecular machinery, are important for facilitating bioengineering and breeding to meet the growing global food demand. The BioCro family of crop models are semi-mechanistic models scaling from biochemistry to whole crop growth and yield. BioCro was previously parameterized and proved effective for the biomass crops miscanthus, coppice willow, and Brazilian sugarcane. Here, we present Soybean-BioCro, the first food crop to be parameterized for BioCro. Two new module sets were incorporated into the BioCro framework describing the rate of soybean development and carbon partitioning and senescence. The model was parameterized using field measurements collected over the 2002 and 2005 growing seasons at the open air [CO2] enrichment (SoyFACE) facility under ambient atmospheric [CO2]. We demonstrate that Soybean-BioCro successfully predicted how elevated [CO2] impacted field-grown soybean growth without a need for re-parameterization, by predicting soybean growth under elevated atmospheric [CO2] during the 2002 and 2005 growing seasons, and under both ambient and elevated [CO2] for the 2004 and 2006 growing seasons. Soybean-BioCro provides a useful foundational framework for incorporating additional primary and secondary metabolic processes or gene regulatory mechanisms that can further aid our understanding of how future soybean growth will be impacted by climate change.

Divergent responses of maize yield to precipitation in the United States

How maize yield responds to precipitation variability in space and time over broader scales is largely unknown compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. Here, we develop a method to quantify the spatially explicit precipitation response of maize yield using statistical data and crop models in the contiguous United States. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39 %), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ~90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change.

Accurate specification of water availability shows its importance for global crop production

It is well established that warming temperatures damage the yields of many crops across the globe. Yet the influence of water supply on global agricultural yield and its relation to water demand and direct temperature stress is unclear. A number of global studies found a minor influence for precipitation, whereas some regional analyses suggest a more prominent role for water availability. Here, we use satellite-based measurements to quantify how soil moisture and temperature jointly influence the global productivity of maize, soybeans, millet, and sorghum. Relative to empirical models using precipitation as a proxy for water availability, models using soil moisture better separate water supply stress from correlated heat stress, leading to a 30 to 120% increase in explained variance of inter-annual yield anomalies across crops. Historic yield anomalies are equally determined by temperature and soil moisture, whereas projected damages associated with climate change are substantially larger for temperature. Globally, yield damages of -9% to -32% are predicted across crops under SSP5-8.5 between 2015-2035 and 2080-2100. Projections using temperature and precipitation, instead of soil moisture, overestimate the magnitude of damages to agricultural productivity because they confound heat stress and dryness stress, and because dryness associated with historically hot days is proportionately more severe than that expected for global warming. These findings indicate that use of remotely sensed measurements of soil moisture improve the representation of water supply in empirical crop models and document the importance of accurately measuring and modelling the influence of water supply to predict historic and future changes in global agricultural productivity.

Topological Sustainability of Crop Water Requirements and Irrigation Scheduling of Some Main Crops Based on the Penman-Monteith Method

The following method was used to apply the topology of the current study of evapotranspiration ETo, net irrigation demand, irrigation schedules, and total effective rain fall of different crop models: using the Food and Agriculture Organization's (FAO) CROPWAT 8.0 standard software and the CLIMWAT 2.0 tool and the FAO-56 Penman-Monteith approach to examine the variable topology of evapotranspiration ETo. Due to high temperatures in summer with an annual mean of 6.33 mm/day, the topological demonstration of reference evapotranspiration (ETo) increases from 2.84 mm/day in January to a maximum of 9.61 mm/day in July. Effective rainfall fluctuates from 0 mm to 53.4 mm. Total irrigation topological indices requirements were 308.3 mm/dec, 335.9 mm/dec, 343.6 mm/dec, 853 mm/dec, and 1479.6 mm/dec for barley, wheat, maize, rice, and citrus, respectively. The physical topological indices due to low demand in winter and high demand in summer, the total net irrigation, and gross irrigation for clay loamy soils for wheat (210.6 mm and 147.4 mm), barley (176.6 mm and 123.6 mm), citrus (204.5 mm and 143.2 mm), and maize (163.9 mm and 114.7 mm), but not for rice. This topology demonstrates that wheat has 4, barley has 4, citrus has 12, maize has 4, and rice crop has 12 irrigation schedules in a year.

Crop Models: Important Tools in Decision Support System to Manage Wheat Production under Vulnerable Environments

Decision support systems are key for yield improvement in modern agriculture. Crop models are decision support tools for crop management to increase crop yield and reduce production risks. Decision Support System for Agrotechnology Transfer (DSSAT) and an Agricultural System simulator (APSIM), intercomparisons were done to evaluate their performance for wheat simulation. Two-year field experimental data were used for model parameterization. The first year was used for calibration and the second-year data were used for model evaluation and intercomparison. Calibrated models were then evaluated with 155 farmers’ fields surveyed for data in rice-wheat cropping systems. Both models simulated crop phenology, leaf area index (LAI), total dry matter and yield with high goodness of fit to the measured data during both years of evaluation. DSSAT better predicted yield compared to APSIM with a goodness of fit of 64% and 37% during evaluation of 155 farmers’ data. Comparison of individual farmer’s yields showed that the model simulated wheat yield with percent differences (PDs) of −25% to 17% and −26% to 40%, Root Mean Square Errors (RMSEs) of 436 and 592 kg ha−1 with reasonable d-statistics of 0.87 and 0.72 for DSSAT and APSIM, respectively. Both models were used successfully as decision support system tools for crop improvement under vulnerable environments.