Soil Water Information in Farmers' Hand?-?Using EO data and related Geospatial Information for Precision Water Management
Plants absorb varying amounts of water throughout their life cycle. For a typical day, most of the water requirement is absorbed in the morning, and for a cropping season, in the middle when it’s warmer. Several factors influence the rate of water use by the plant: rainfall, hours of sunshine, temperature, wind, and humidity. However, the plant will not uptake its water requirement if it’s inadequate or unavailable in the soil. The consequences of this uncertainty in crop production range from germination failure to a reduction in yield potential, which exacerbates an already worrying food insecurity situation in the world.
There’s a growing number of people who are food insecure in the world, leading to hunger and poor nutrition (FAO, IFAD, WFP, 2014) (Croft et al., 2016), and this is further compounded by climate change. Food and nutritional security are major global concerns, but they are predominant in developing countries (Kisaka-Lwayo & Obi, 2016). Water is an essential need for crop production. It is responsible for several important functions at the tissue level.
Agriculture is the largest water-consuming activity, accounting for about one-third of all freshwater withdrawals globally. It requires even more water to sustain the continuous demand for food fostered by population growth and changes in rainfall and temperature patterns.
A sustainable solution for on-farm water management must include a responsive mechanism for measuring how much water is available in the soil, how much is added, and how much is removed. This is important because we can only manage what we cannot measure. The proposed methodology combines Earth observation data with other multivariables to evaluate soil moisture availability. This differs from other, more traditional approaches that measure water losses through evaporation from soil and transpiration by crops as a means to determine available water resources. A persistent measurement of soil moisture at the pixel level presents a unique window for an efficient, precise timing of irrigation schedule, and to do so at scale accurately and affordably, on a crop by crop basis.
Improved monitoring of water availability can minimize crop vulnerabilities and maximize yield potential, thereby improving farmers’ resilience to climate change and other related uncertainties.
To this end, expanding the capacity of farmers to produce more food for increased income at a low cost using easily accessible technologies without further damage to the environment should be prioritized.
Theoretical Background
There are several methods deployed for calculating soil moisture content; the most common involves the use of moisture sensors or direct measurement (gravimetry). Similarly, remote sensing of soil moisture is favored by researchers and increasingly adopted by farmers. It offers some unique advantages, such as greater spatial and temporal accuracy at a lower cost and unmatched scalability. It can measure soil moisture over a wider coverage area than any conventional method, efficiently and cost-effectively.
In order to take advantage of remote sensing for crop water management, it’s important to understand the crop’s water needs both in terms of amount and timing. For example, most grain crops will uptake water from the soil at a slower rate in the early phenophase; this rate rapidly accelerates as it approaches the end of the vegetative phase and rapidly slows down as the reproduction phase proceeds to completion.
What is also interesting is the direct correlation between the rate of nutrient uptake (e.g., nitrogen) and that of water uptake. Where there’s inadequate soil moisture, it will impact nutrient uptake commensurately. While this information provides the requisite head-up for farmers across seasons, it is particularly relevant in irrigation farming scenarios. This underscores the need for monitoring the available soil moisture at a spatio-temporal scale. Earth observation, or EO (via remote sensing), makes it possible to create soil moisture maps at high-resolution. This empowers farmers with information on where deficiency exists and when, thereby protecting yield potential and improving production efficiency.
There are up to half a dozen algorithms for generating soil moisture (SM) maps using EO data; the resulting common indices used include (but are not limited to): Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Other moisture-related indices such as the Normalized Difference Moisture Index (NDMI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), and Structure Insensitive Pigment Index (SIPI) have been used to establish relationships with SM.
The Approach
For this presentation, we will be demonstrating LAI-based soil moisture index maps and their relationship to crop health. The full details of the algorithm are not disclosed; however, the design diagram below gives an idea of the approach adopted.
It is general knowledge that the finer the soil texture, the higher its moisture retention (due to more pores). Besides soil texture, other soil information determines its moisture condition, particularly the total available water and the wilting point. This means the soil moisture map from remote sensing is not enough information, we also need to take into account the soil information and climate pattern on a temporal scale. That way, we can provide the farmer with complete and balanced information on soil moisture in a way that is comprehensive. This is important as it shows the causative factor in addition to the effect; for example, when the soil moisture map shows high water stress, it could have been a consequence of soil texture. In this way, the decision support system does not advise the farmer to water the soil, which amounts to a waste of water.
In a similar vein, climatic conditions also play an important role in soil moisture content. For example, at lower temperatures, soil moisture content is higher (and vice versa). Needless to add, soil moisture content is critical before, during, and after sowing; the crop type and geographic location determine the optimal amount required. A general rule of thumb is that the optimal planting window is at a time when the rate at which moisture is added to the soil is slightly higher than the rate at which it is removed. The scenario is quite different post-planting.
领英推荐
Discussion
From the above side-by-side comparison of the soil moisture map and the normalized difference vegetation index, there is a direct correlation between soil moisture content and crop health. Drawing this conclusion does not suggest other factors would not be at play in this scenario, such as: pests, diseases, nutrient deficiency, and other factors. Knowing that water stress is not a causative factor removes one item from the checklist, enabling the farmer to further narrow down the list of what could be wrong when it is identified timely and efficiently.
Implementation Checklist
References
Ratliff, L. & Ritchie, Joe & Cassel, D.. (1983). Field-Measured Limits of Soil Water Availability as Related to Laboratory-Measured Properties1. Soil Science Society of America Journal — SSSAJ. 47. 10.2136/sssaj1983.03615995004700040032x.
Hanson, Paul & Edwards, N. & Garten, C. & Andrews, J.A.. (2000). Hanson PJ, Edwards NT, Garten CT, Andrews JA.. Separating root and soil microbial contributions to soil respiration: A review of methods and observations. Biogeochemistry 48: 115–146. Biogeochemistry. 48. 115–146. 10.1023/A:1006244819642.
Cao, F.X. et al., 2017. Linear vs. Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Images. Sensors, 17, p.2603.
Chukwuji, C.O., Inoni, O.E., Ogisi, O.D. & Oyaide, W.J., 2006. A Quantitative Determination of Allocative Efficiency in Broiler Production in Delta State, Nigeria. Agriculturae Conspectus Scientificus, 71, pp.21–26.
Croft, M.M., Marshall, M.I. & Steven, H.G., 2016. Market Barriers Faced by Formal and Informal Vendors of African Leafy Vegetables in Western Kenya. Journal of Food Distribution Research, 47, pp.49–60.
FAO, IFAD, WFP, 2014. Strengthening the enabling environment for food security and nutrition. The State of Food Insecurity in the World 2014. Rome: FAO.
Jia, S.Y. et al., 2017. Hyperspectral Imaging Analysis for the Classification of Soil Types and the Determination of Soil Total Nitrogen. Sensors, 17, p.2252.
Nwafor, C.U. & van der Westhuizen, C., 2020. Prospects for Commercialization among Smallholder Farmers in South Africa: A Case Study. Journal of Rural Social Sciences, 35, p.2.
Oguike, P.C. & Mbagwu, J.S.C., 2009. Variations in Some Physical Properties and Organic Matter Content of Soils of Coastal Plain Sand under Different Land Use Types. World Journal of Agricultural Sciences, 5, pp.63–69.
Powlson, D.S. et al., 2016. Does conservation agriculture deliver climate change mitigation through soil carbon sequestration in tropical agro-ecosystems? Agriculture, Ecosystems and Environment, 220, pp.164–74.
Pullanagari, R.R., Kereszturi, G. & Yule, I.J., 2017. Quantification of dead vegetation fraction in mixed pastures using AisaFENIX imaging spectroscopy data. Int. J. Appl. Earth Obs., 58, pp.26–35.
Song, Y.-Q. et al., 2018. Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale. Sensors, 18, p.3086.
Udom, B.E. & Omovbude, S., 2019. Soil physical properties and carbon/nitrogen relationships in stable aggregates under legume and grass fallow. Acta Ecologica, 39, pp.56–62.
VandenBygaart, A.J. & Angers, D.A., 2006. Towards accurate measurements of soil organic carbon stock change in agroecosystems. Canadian Journal of Soil Science, 86, pp.465–71.
Yu, X. et al., 2016. Evaluation of MLSR and PLSR for estimating soil element contents using visible/near-infrared spectroscopy in apple orchards on the Jiaodong peninsula. Catena, 137, pp.340–49.
Research assistant | Open innovation Agri-food| consultant
1 年Thanks bro Gabriel