Crop modeling

Crop models are computer programs that simulate the growth and development of crops by predicting the growth of individual components of a plant such as leaves, roots, stems, and grains. The modeling process uses equations or a series of equations to describe a system’s behavior (Oteng-Darko et al., 2013). The crop’s growth mechanisms are influenced by combining reactions and interactions between tissues and organs (Oteng-Darko et al., 2013). Crop models have been used in many research instances globally to measure climate impacts on crops (Zinyengere et al., 2015).

Why crop modeling?

Crop growth simulation models help evaluate the effects of climate change and agronomic management on the future crop production. As projections show a rise in the planet’s temperature with increased carbon dioxide and other greenhouse gas concentrations, global crop yields and productivity will be hampered (Nath et al., 2017). The effect on plants comes because plants’ biological processes of respiration, photosynthesis, plant development, reproduction, water use, and others are all affected by increased temperature and carbon dioxide concentrations. As a result, such effects will influence biomass partitioning and the plant’s overall growth and development. Therefore, crop modeling would aid scientists in guiding farmers’ choice of crop management decisions such as crop selection, cultivar selection, sowing regimes, and irrigation scheduling to reduce risks of crop failure and ensure resilient farming systems  (Oteng-Darko et al., 2013).

As a research tool, model development and application can identify gaps in our knowledge, thus enabling more efficient and targeted research planning (Jones et al., 2003). The model can be used to effectively conduct research that would, in the end, save time and money spent in the field evaluating new cultivars and technologies. Therefore, crop modeling contributes to the speedy development of better sustainable agriculture practices that meet the world’s needs for food and other services (Oteng-Darko et al., 2013, Jones et al., 2003, Jones et al., 2016).

Model Parameterization

This concept involves calibration, evaluation, and validation of parameters in the model. Model calibration entails changing specific model parameters so that the data simulated by the error-free model matches the observed data. The model validation phase verifies that the calibrated model accurately depicts the real-world scenario. The technique entails comparing simulated performance and previous data from the calibration point (Jones et al., 2003). After one is confident that the models simulate the real world adequately, computer experiments are conducted hundreds or thousands of times for given environments to determine how to best manage or control the system (Jones et al., 2003).

Soybean Modelling Tools

Different crop models vary in their complexity and performance, thus their sensitivity to systematic variations in climatic change (Battisti, 2016, Battisti et al., 2018). These models are used for various purposes, including analyzing crop management practices in relation to meteorological conditions. Examples of models used for most plants are FAO – Agroecological Zone, AQUACROP, DSSAT CSM CROPGRO, APSIM, MONICA, PEGASUS, etc. These tools model future crop production as sole models or ensembles to give average projections.

The Decision Support System for Agrotechnology Transfer (DSSAT) Crop Simulation Model (CSM)

The DSSAT model is a software program that comprises dynamic crop growth simulation models (Jones et al., 2017). The International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT) developed the tool and is one of the most widely used modeling tools (Jones et al., 2003). This network developed the tool after considering that the conventional agronomic research methods for generating data and publishing are insufficient to fulfill the growing research needs of modeling. Traditional agronomic studies are performed at specific times and locations, resulting in findings that are site and season-specific, time-consuming, costly, and under experimental trial and error (Jones et al., 2003). After ensuring that perhaps the models accurately replicate the real world, users can run computer experiments many times for specific conditions to decide the best way to operate or manipulate the device.

Dataset Required to do DSSAT Modelling

Various data is required in the process of running simulations as depicted in the figure above. however, the following are the primary datasets that are necessary to produce reasonable answers and to be easily collected in field situations(Jones et al., 2016):

• Data to Run the model

Weather data: Daily maximum/minimum temperature, precipitation, and solar radiation

Soil characteristics: Soil texture, bulk density, organic matter, nitrogen, and thickness for the soil horizon

Crop management data: Crop, cultivar/variety, planting date, row, plant spacing, irrigation dates and amounts, fertilizer and chemical applications, tillage operations, harvest.

• Data for Model Evaluation

Crop measurements: Yield and yield components (seed size and weights), phenology (flowering, physiological maturity dates).

For growth and partitioning: Biomass components (leaf, stem, seeds), Measurements of photosynthesis, canopy temperature

For Irrigation or fertilizer response: Soil measurements (moisture of different depths over time), soil nitrogen, Phosphorus, carbon.

References

BATTISTI, R. 2016. Calibration, uncertainties, and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil. Universidade de São Paulo.

BATTISTI, R., SENTELHAS, P. & BOOTE, K. 2018. Sensitivity and requirement of improvements of four soybean crop simulation models for climate change studies in Southern Brazil. International journal of biometeorology, 62, 823-832.

JONES, J. W., ANTLE, J. M., BASSO, B., BOOTE, K. J., CONANT, R. T. & FOSTER, I. 2016. Brief history of agricultural systems modeling. Agricultural Systems, 155, 240-254.

JONES, J. W., ANTLE, J. M., BASSO, B., BOOTE, K. J., CONANT, R. T., FOSTER, I., GODFRAY, H. C. J., HERRERO, M., HOWITT, R. E. & JANSSEN, S. 2017. Brief history of agricultural systems modeling. Agricultural systems, 155, 240-254.

JONES, J. W., HOOGENBOOM, G., PORTER, C. H., BOOTE, K. J., BATCHELOR, W. D., HUNT, L., WILKENS, P. W., SINGH, U., GIJSMAN, A. J. & RITCHIE, J. T. 2003. The DSSAT cropping system model. European journal of agronomy, 18, 235-265.

NATH, A., KARUNAKAR, A., KUMAR, A., YADAV, A., CHAUDHARY, S. & SINGH, S. P. 2017. Evaluation of the CROPGRO-soybean model (DSSAT v 4.5) in the Akola region of Vidarbha, India. Ecology, Environment and Conservation, 23, 153-159.

OTENG-DARKO, P., YEBOAH, S., ADDY, S., AMPONSAH, S. & DANQUAH, E. O. 2013. Crop modeling: a tool for agricultural research–A. J Agric Res Dev, 2, 001-006.

ZINYENGERE, N., CRESPO, O., HACHIGONTA, S. & TADROSS, M. 2015. Crop model usefulness in drylands of southern Africa: an application of DSSAT. South African Journal of Plant and Soil, 32, 95-104.

Understanding the Soil and Climate change

Properties of healthy and quality soil

Soil is simply defined as the medium for plants growth. The optimum growth of plants occurs on soils that are healthy enough to offer plants protection from different ailments and nutritional support. A healthy soil is the one that offers a combination of physical, chemical, and biological support processes. These characteristics help to promote ecological functions that are monitored in sustainable landscape management and climate change. Understanding the relationship between soil health and climate change is crucial for increased yields and, eventually, food security. https://extension.umaine.edu/cranberries/grower-services/workshops-and-meetings/berry-soil-management/ 

According to Allen et al. (2011), aggregate stability, soil organic matter (SOM), carbon and nitrogen cycling, microbial biomass and activity, as well as microbial fauna and flora variety are the common soil health markers that are affected by climate change. The impacts of global climate change drivers such as elevated temperature and CO₂, varying precipitation, and atmospheric nitrogen are considered in the interaction.

The soil physical properties such as soil structure, porosity, infiltration, and bulk density affect some soil processes such as the availability of water in the soil that influence plant growth. Chemical soil properties such as pH, plant-available nutrients, salinity affect the soils by influencing the microbial activities and nutrients availability. The biological soil properties consider the microbial life of the soils that influence the decomposition of soil organic matter and then the availability of total soil carbon and nitrogen.

Soil types available in various agro-ecologies and altitudes differ in their properties and such variations make such agro-ecologies differ in their resilience to climate changes (Franke et al., 2018). These variations in combination with other local biophysical and social-economic conditions have been highlighted to be the cause of the varying adoption rates of soil fertility improvement practices under conservation agriculture (Waha et al., 2013, Müller et al., 2011). Mostly, these practices are universally promoted in the different agro-ecologies and their adoption has been reported lower than expected in most of the cases (Thierfelder et al., 2015).

The findings of the study I recently did on ‘modeling the impacts of integrating soybean (glycine max l.) in maize (Zea mays) cropping systems in the agro-ecological regions of Zambia’s show that soils containing fine particles like clay have the ability to support plant growth under relatively low moisture levels. Such higher yields resulted from the presence of a layer of clay accumulation that is existing within the rooting levels which contain high amounts of accessible nutritional ions such as calcium, magnesium, sodium, or potassium that support the growth of maize (Krogh and Greve, 1999). Clay has a relatively higher capacity of retaining soil organic compounds due to the larger surface area of its colloids that carry binding charges, thereby chemically stabilizing organic materials. This characteristic is different in soils that have a larger proportion of course particles.

Management practices on improving soils with higher coarse particles include reduced soil disturbance and maintenance of the above-ground crop residues. These practices should be promoted to ensure that such soils with higher course particle content have higher soil organic matter at the surface and enhance the improvement of the soil properties which make plants resilient to climate change impacts. Such being the case, precise and localized information on soil types and the associated climate change consequences may guide smallholder farmers in planning better farm interventions to adapt to their region-specific effects of climate change.

References

ALLEN, D. E., SINGH, B. P. & DALAL, R. C. Soil Health Indicators Under Climate Change: A Review of Current Knowledge. Soil Biology, 25.

FRANKE, A., VAN DEN BRAND, G., VANLAUWE, B. & GILLER, K. 2018. Sustainable intensification through rotations with grain legumes in Sub-Saharan Africa: A review. Agriculture, ecosystems & environment, 261, 172-185.

KROGH, L. & GREVE, M. 1999. Evaluation of World Reference Base for Soil Resources and FAO Soil Map of the World using nationwide grid soil data from Denmark. Soil Use and Management, 15, 157-166.

MÜLLER, C., CRAMER, W., HARE, W. L. & LOTZE-CAMPEN, H. 2011. Climate change risks for African agriculture. Proceedings of the National Academy of Sciences, 108, 4313-4315.

THIERFELDER, C., RUSINAMHODZI, L., NGWIRA, A. R., MUPANGWA, W., NYAGUMBO, I., KASSIE, G. T. & CAIRNS, J. E. 2015. Conservation agriculture in Southern Africa: Advances in knowledge. Renewable Agriculture and Food Systems, 30, 328-348.

WAHA, K., MÜLLER, C., BONDEAU, A., DIETRICH, J. P., KURUKULASURIYA, P., HEINKE, J. & LOTZE-CAMPEN, H. 2013. Adaptation to climate change through the choice of cropping system and sowing date in sub-Saharan Africa. Global Environmental Change, 23, 130-143.

FOOD SYSTEMS AND NATURE

IMPROVING AGROFORESTRY BY ENHANCING ITS REGENERATIVE CAPACITY

The natural system is the composition of the earth’s plants, animals, landscape, and climatic features that work in an inter-related manner with humanity. The failure of components of the natural system negatively affects humanity. The well-functioning food system supports healthy humanity in a healthy environment, thereby attaining economic and cultural health.

As part of actions towards achieving Sustainable Development Goals (SDGs) by 2030, the Food Systems Summit (FSS) convenes in 2021 to launch its actions of delivering their progress. Among its five actions, the goal of Action 3 of the 2021 FSS is to develop innovations that would boost nature-positive production. Thus, this improves the environment’s capability to offer enough resources for food production, reducing biodiversity loss, pollution, water use, soil degradation, and greenhouse gas emissions.

In line with this goal, scaling up agroforestry by enhancing its regenerative capability is an innovative way to facilitate this action track. This sentiment agrees with a paper by FAO (2018) which recognizes that agroecology presents a unique methodology to transform agri-food systems sustainably and comprehensively. Such being the case, the following points highlight the reasons behind this choice and ways to implement this innovation successfully.

Why upscale agroforestry?

For a long time, farmers have been advised on the importance of crop diversification while restoring the degraded land by including trees and shrubs into their crop and animal farming systems, a practice also known as agroforestry (Elevitch et al., 2018, Dagar and Tewari, 2016). However, there has been a slow adoption of the same due to undervaluing trees’ value and inadequate awareness (Lovell et al., 2018). Below are some of the regenerative elements to be scaled up in order to improve agroforestry.

Benefits of agroforestry

Proper design, implementation, and management of agroforestry yield the following regenerative goals, as also highlighted by Dagar and Tewari (2016):

• Enriching soil

It allows for the restoration of nutrients and rebuilding of soil health into the soil through different ways. Leguminous trees fix nitrogen into the soil, thereby preventing the excessive use of inorganic fertilizers. The soil microbes thrive, and their movements improve aeration, thereby aiding the thorough respiration of plants. The microbes also aid the decomposition of the dry matter into essential elements required by plants which are released gradually into the soil.

• Enhancing water quality

The crop cover in agroforestry systems prevents excessive evaporation, thereby providing a moist medium for the plants’ roots environment longer hence efficiently using the water better than where the land is bare, ensure increased infiltration and prevent excessive runoff that erodes the soil and thereby allowing clean water to fill the water bodies.

• Conserving biodiversity

Good soil health and water quality are vital for living things such as trees, pollinators, and microbes, which can also coexist. The niche created builds a microbiome where pest and disease pathogens do not thrive. 

• Preserving ecosystems (services)

Symbiotic relationships exist amongst the living things (plants, microbes, birds, insects) in the agroforestry environment, where they form a niche. Other services provided are regulating, provisioning, supporting and enhancing culture as in figure.

• Carbon sequestration

Agroforestry plays a significant role in biological carbon sequestration. The presence of plants and trees that continuously photosynthesize helps absorb carbon dioxide from the environment, thereby contributing to greenhouse gas reductions hence contributing to climate change mitigation (Schoeneberger et al., 2017). 

Other benefits

• Increased crop yields, improved nutrition and Improved rural economies:

As the soil environment gets enriched with nutrients and water availability, the crops yield better and farmers sell the surplus hereby allowing farmers to buy other nutritious foods. It also ensures developed households which extends to the whole communities. This agrees with (Schoeneberger et al., 2017) who highlighted that agroforestry allows for diversified income from fruit trees, crop yields, and livestock on top of enhancing nature.

• For recreation and poverty alleviation

Agroforestry offers the opportunity to intensify agriculture by restoring neglected forests and landscapes (Sahoo et al., 2020). The agricultural intensification boosts food security and income, and ensure climate mitigation and biodiversity preservations (Tubenchlak et al., 2021). This suggests that agroforestry drives the regeneration of degraded landscapes in human-dominated areas while providing scenery that beautifies the environment.

• For women empowerment

In most of developing countries, most households depend on firewood for cooking whose collection is women’s responsibility (Benjamin et al., 2018). Agroforestry offers the opportunity of firewood sources near homesteads. This allows women to have more time to be involved in other development duties other than wasting time in search for firewood.

Suggested ways of scaling up adoption rates of agroforestry

• Create awareness on the importance of adopting agroforestry and its relationship to regenerative agriculture.
• Motivating farmers to be part of the solution by incentivizing their activities.
  • By offering farm inputs to farmers willing to venture into agroforestry
  • By compensating restorative farm operations according to quality of production instead of volume or mass of production (Lovell et al., 2018)
  • By frequent visits to farmers practicing agroforestry
  • By encouraging exchange visits
• Integrating nut and fruit tree species in the agroforestry system with shorter fruiting periods that provides incomes and shorter return rates to encourage adoption (Lovell et al., 2018).

REFERENCES

BENJAMIN, E. O., OLA, O. & BUCHENRIEDER, G. 2018. Does an agroforestry scheme with payment for ecosystem services (PES) economically empower women in sub-Saharan Africa? Ecosystem Services, 31, 1-11.

DAGAR, J. & TEWARI, J. 2016. Agroforestry research developments: anecdotal to modern science. Agroforestry research developments. Nova Publishers, New York, 1-45.

ELEVITCH, C. R., MAZAROLI, D. N. & RAGONE, D. 2018. Agroforestry standards for regenerative agriculture. Sustainability, 10, 3337.

FAO 2018. Scaling up agroecology initiative: Transforming food and agricultural systems in support of the SDGs. Food and Agriculture Organization of the United Nations Rome, Italy.

LOVELL, S. T., DUPRAZ, C., GOLD, M., JOSE, S., REVORD, R., STANEK, E. & WOLZ, K. J. 2018. Temperate agroforestry research: considering multifunctional woody polycultures and the design of long-term field trials. Agroforestry Systems, 92, 1397-1415.

SAHOO, G., WANI, A. M., SHARMA, A. & ROUT, S. 2020. Agroforestry for Forest and Landscape Restoration.

SCHOENEBERGER, M. M., BENTRUP, G. & PATEL-WEYNAND, T. 2017. Agroforestry: enhancing resiliency in US agricultural landscapes under changing conditions. Gen. Tech. Report WO-96. Washington, DC: US Department of Agriculture, Forest Service, 96.

TUBENCHLAK, F., BADARI, C. G., DE FREITAS STRAUCH, G. & DE MORAES, L. F. D. 2021. Changing the Agriculture Paradigm in the Brazilian Atlantic Forest: The Importance of Agroforestry. The Atlantic Forest. Springer.

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