Leaf Area Index accuracy is one of the important parameters for correct yield prediction. At the same time, there are still so many questions about how to get it right? And how to use the most efficient methods to calculate leaf area index (LAI)? What is optimum LAI? Which methods to estimate leaf area index accuracy are better? But, let’s go deeper in this topic and get numbers and facts to understand how to measure LAI effectively and accurately.
This is a cornerstone question of plant science and crop research. Why is Leaf Area Index so important? What is the value of knowledge for this index?
The answer is simple as usual. Leaf Area Index (or LAI) is a proportion between two values – amount of green leaves and amount of soil. In other words, it helps to understand the density of green coverage in comparison with ground. Also, it helps to understand the level of photosynthesis activity of a plant and evaporation of water from leaves. As a result, scientists agree that leaf area index is one of the core characteristics of a plant.
The leaf area index is of topmost significance for eco-physiology in many ways: in demonstrating, it serves as a scaling factor, controlling processes like photosynthesis and evapotranspiration. A review on leaf area index of horticulture crops and its importance
Leaf area index (LAI) is one of the most important variables to assess canopy structure in terms of accurate modelling of energy balance, gas exchange processes and light distribution occurring within the canopy of fruit orchards. It is also a key variable used by a variety of physiological and functional plant models for agronomy and horticulture and by remote sensing models at large scales for ecology.
The significance of the LAI is directly related to the importance of leaves to the plants. In fact, leaves are the major eco-physiological parts of any plant that interacts with the components of atmosphere. The main task of leaves are to:
Leaves are the major eco-physiological parts of any plant that interacts with the components of atmosphere. Hence, LAI helps to assess this interaction Leaves are the major eco-physiological parts of any plant that interacts with the components of atmosphere. Hence, LAI helps to assess this interaction
Additionally, any leaf is a part of a plant which is responsible for feeding it with carbohydrates. Leaves produce carbohydrates during photosynthesis and then a whole plant converts it to a myriad of chemicals for its needs.
As a result, Leaf Area Index (LAI) is a core indicator of four processes: radiation interception, precipitation interception, energy conversion, and water balance.
Finally, LAI is a reliable parameter for plant growth. This is one of the reasons why most studies in agronomy, horticulture and ecology measure Leaf Area Index to represent the results of interventions. For example, application of fertilisers and irrigation or expression of the yield.
Leaf Area Index is an important measure of canopy structure because tree morphology, leaf orientation and distribution influence LAI estimates. Therefore, when checking Leaf Area Index accuracy, it is important to remember the following aspects:
Trees of different species can have very different LAI values. For example, in the same experiment, the average leaf area index for Ultra Red Gala apple trees which are 3.8–4.0 m tall, was 2.46. However, for Ultra Red Gala apple trees, which are 2.5–3.0 m tall, the average LAI was 2.96.
As mentioned above, the levels of LAI will vary with the canopy architecture. Respectively, it depends on the cultivars, geography as well as field growing practices and conditions. Then there are several differences which arise from the types of crops and fruits. In practice, there is more available data about about the optimum LAI for cereals than fruits, for example:
Apples on average have Leaf Area Index between 1.5 and 5. LAI for peaches can be 7 to 10. Mangoes’ LAI is averagely 2.94 and it can lie between 1.18 and 4.48. Leaf Area Index for Oranges is higher and can be between 9 and 11.
In fact, it was scientifically proved that varieties with erect leaves have a higher optimum LAI than crops and fruits with horizontal leaves.
The optimum leaf area index (LAI) at beginning bloom to reach the yield potential is between 3.6 (indeterminate stem termination) and 4.5 (determinate stem termination). The optimum LAI maximum to reach the yield potential is between 6.0 and 6.5 for indeterminate and determined cultivars.
Indeed, the leaves are essential for photosynthesis, water evapotranspiration and biomass production. As a result, the number of leaves and leaf area index (LAI) will also have impact on forecast about yield. Moreover, most crop simulation models use LAI on routine basis to predict yield. Indirectly, this aspect proves the importance of leaf area index accuracy in estimating the effect of different environmental factors on plants.
Photosynthesis is the primary determinant of crop yield. Its efficiency by which a crop captures light and converts it into biomass over the growing season is a key determinant of final yield, be that biomass or grain Feeding the world: improving photosynthetic efficiency for sustainable crop production
At the same time, the relationship between LAI of leaves and yield is not so simple and straightforward. Leaf area index varies with kinds of crops and is different depending on the life-stage of a plant. As a result, leaf area index has to be measured in different phases of the plant cycle for yield forecast to accurately calculate the optimum yield. In fact, historical comparisons of leaf area index, measured in different years, is an excellent way to assess the crop production and effectiveness of farming methods.
Depending on the type of crop, value of higher or lower LAI brings different results. For example, if the yield production is based on leafy greens, then LAI is a selling point by itself. However, when the farming business is based on production of fruits and vegetables, then leaves sometimes could be a subproduct. However, most of the time, in fruit farming and vegetable production the green biomass is not generating any income. In this case, leaves do not impact the yield yield directly. In any of the mentioned cases, LAI and leaf area index accuracy influence can vary.
In the case of crops where the farming business is based on selling leafy parts to get maximum leaf area index is the core task. In other words, higher LAI brings direct benefit for farmer due to higher value of the production. For example, this is relevant for spinach, lettuce, arugula, beet greens, kale, watercress, etc.
For younger trees it is crucially important to increase LAI. It will boost fruit yield and overall production. However, in a case, when the canopy becomes extremely thick and massive, it may have negative impact on yield. In particular, it prevents light penetration to lower level of tree and to developing fruits (for example, in apple trees). At the same time, in older fruit trees there is an increase in yield quantity as well as yield quality.
In many vegetables, higher photosynthesis is linked to higher yield. Therefore, a higher LAI is needed. In vegetables such as tomatoes, decreasing LAI can impact biomass accumulation and yield. However, it is important to maintain only the optimum LAI, as increasing LAI beyond a certain point will not increase yield. For example, for tomatoes LAI between 3 and 4 is good, depending on the variety of crop.
For cereals LAI is crucially important because it determines biomass accumulation. At the same time, in cereals an optimum LAI is sometimes a better goal. This depends on the variety of crop. Particularly, sometimes as increasing LAI may not increase photosynthesis due to shading. But simultaneously it contributes to increased respiration.
For example, in rice it is highly required to increase LAI and photosynthesis. But in sorghum, this requirement is completely different. The relationship between LAI and yield is high but negative. As a result, increasing leaf area index in sorghum will decrease yield. Increasing Leaf Area Index accuracy (LAI) in rice will help to gain better yield. Increasing Leaf Area Index (LAI) in rice will help to gain better yield. Photo Credit: Quang Nguyen Vinh
Over the past 50 years, agricultural yields of major crops have risen keeping up with demand. For the most part, these increases came about due to advances in agronomic approaches and classical breeding. For example, mainly there was a focus on maximized plant architecture and light capture, resulting in higher yielding varieties. However, despite the efforts, in the last years increase in yields of the major crops in many parts of the world is not increased anymore. It is plateaued, as a result there is a need to develop higher yielding varieties. This will help maintain the supply of food required to meet the needs of the growing population
At the same time, considerations about leaf growth and fruit production between leaf growth and fruit production has assumed added relevance due to climate change. Due to the increasing levels of carbon dioxide (CO2), many cultivars are devoting too much of their resources to making leaves rather than seeds. For example, in soybeans, the fourth most cultivated seed crop, there has been a decrease of 8% to 10% in yield due to recent conditions of elevated CO2.
It is possible to measure LAI directly or indirectly. In direct methods, the leaves are used as the basis of measurement. For indirect methods, it is possible to use automated non-contact digital devices.
Measuring leaf area index directly can be destructive or non-destructive.
If you want to measure leaves destructively, you need to do this by harvesting leaves. Cut leaves from the plant and measure their leaf area index.
In non-destructive method, there is no active action. You do not need either cut or harvest leaves by yourself. All you need to do is to place the traps and then collect them in traps on the ground.
Some of direct methods of LAI estimation are time-consuming and difficult. Also, there are issues with leaf area index accuracy and limitations to count many leaves in a short period of time.
There are three concepts for leaf area calculation. Each of them requires different level of equipment and knowledge. Hence, not all these concepts are relevant for beginners in crop research.
Core method for measurement of leaf area index, as mentioned before, can be destructive or non-destructive. However, in any case the key object is a leaf and its area is measured manually or using leaf area meter. There is a wide range of leaf area meters – handheld or digital to measure leaf area index.
You need to measure leaf perimeter by a planimeter. Then you can find leaf area using a special formula and after that – calculate the ratio to the ground.
This method is based on the estimation of the relationship between biomass and the leaf area. The simples way to find biomass is to find the dry weight of leaves.
LAI calculation can be indirect. The most valuable features of indirect estimation of leaf area index is its non-contact and fast work. Additionally, there are ways to automate this task, therefore, improve productivity of the process. There are a couple of the most popular ways for indirect LAI measurement. In particular, point quadrat analysis (or inclined point quadrat) and digital plant canopy analysis. Inclined point quadrat to calculate LAI
This way become widely used in botany and agronomy after 1958. It consist of work with the picture of the vegetation canopy and using a needle. One of the traditional indirect methods to calculate Leaf Area Index Accuracy (LAI) is inclined point quadrat, here is pictured when carried by a crop scientist at vineyard. Photo Credit: Thomas Palleja and Andrew LandersOne of the traditional indirect methods to calculate Leaf Area Index (LAI) is inclined point quadrat, here is pictured when carried by a crop scientist at vineyard. Photo Credit: Thomas Palleja and Andrew Landers
You need to counting the number of contacts made by a needle in a given quadrat. As a result, you will get a measurement of LAI. In practice, this method is time-consuming and suitable only for crops up to 1.5 meters high. Digital plant canopy analysis for LAI measurement
This method is different because it uses photography to measure LAI. It is possible to use the photo from either below or above the canopy. The leaf area index accuracy is based on the images captured by hemispherical lens. This allows to take photos standing under the canopy and calculate LAI based on these photos. Gravimetric measurement of leaf area index accuracy with hemisphere photo Gravimetric measurement of LAI with hemispherical photo
The agriculture industry has radically transformed over the past decade. As a result, toolkit for leaf area index measurement has improved as well. Moreover, LAI has obtained new application. Besides its importance as an indicator of yield and value of crop growth, leaf area index now serves as one of the key parameters for applications in precision agriculture. For example, leaf area index helps to calculate the correct amounts of foliar sprays of pesticides or fungicides that to protect a crop as a part of integrated pest management (IPM). Lack of nutrients in plants is a major issue that needs to be addressed. LAI is one of the parameters to identify the problems with plants growth on early stage. Lack of nutrients in plants is a major issue that needs to be addressed. LAI is one of the parameters to identify the problems with plants growth on early stage.
Leaf area index is also helpful in diagnosing lack of nutrients in cereals (in particular, Nitrogen deficiency) on satellite imagery. That’s why it is reasonable that new methods and tools of estimating LAI are developing to monitor crops and build forecast for yield.
For example, Petiole is one of them. It helps to measure leaf area index directly and get total plant leaf area in taps, without any ImageJ or expensive leaf area scanners.
In relation to the mentioned aspect – price, most of the advanced mobile-based tools like Petiole or Easy Leaf Area are FREE (or have a trial absolutely free of charge). Download Petiole. Leaf Area from Google Play Store
Patil P., Biradar P., Bhagawathi A.U., Hejjegar I.S. (2018). A review on leaf area index of horticulture crops and its importance. International Journal of Current Microbiology and Applied Sciences. 2018; 7(4):505-513. doi.org/10.20546/ijcmas.2018.704.059 Poblete-Echeverría C., Fuentes S., Ortega-Farias S., Gonzalez-Talice J., Yuri A. Digital Cover Photography for Estimating Leaf Area Index (LAI) in Apple Trees Using a Variable Light Extinction Coefficient. Sensors. 2015; 15:2860–2872. doi: 10.3390/s150202860 Simkin A., López-Calcagno P., Raines C. Feeding the world: improving photosynthetic efficiency for sustainable crop production. Journal of Experimental Botany, 2019, 70 / 4, 1119 – 1140. doi.org/10.1093/jxb/ery445 Warren W. Inclined point quadrats. New Phytologist. 1960. 59: 1–8. doi.org/10.1111/j.1469-8137.1960.tb06195.x Yoshida, S. Fundamentals of rice crop science. International Rice Research Institute, Los Baños, Filipinas. 1981. 279 pp