# An Assessment of the Site-Specific Nutrient Management (SSNM) Strategy for Irrigated Rice in Asia

## Abstract

**:**

## 1. Introduction

- Is there evidence of complementary, von Liebig-type relationships among N, P, and K fertilizers?
- Does yield response to N fertilizer application depend on the initial state of the soil?

## 2. Background, Data Description and Empirical Model

#### 2.1. The SSNM Strategy for Rice

#### 2.2. Data Description

#### 2.3. The Empirical Model

^{2}) error term. The N, P, and K fertilizer application by farmers could be endogenous given the unobserved factors that affect yields. There were no good instruments available to address endogeneity concerns regarding the production function estimation. In the presence of heterogeneity, the polynomial and linear plus plateau approximations essentially converge, making the quadratic a viable alternative to the von Liebig and linear response plateau models [36]. Moreover, von Liebig models generally do not fit the data well and the actual estimation does not yield the right-angle isoquants described in its derivation [37].

_{0}cannot be rejected, $\frac{{\partial}^{2}y}{\partial {x}_{i}\partial {x}_{j}}\equiv \frac{\partial}{\partial {x}_{i}}\left(\frac{\partial y}{\partial {x}_{j}}\right)\equiv {\beta}_{ij}\equiv {\beta}_{ji}\equiv 0,$ then it indicates the independence of ${x}_{i}$ and ${x}_{j}$. The marginal productivity of ${x}_{j}$ is not affected by changes in the level of ${x}_{i}.$ If, however, ${H}_{0}$ is rejected, then nutrient interaction between ${x}_{i}$ and ${x}_{j}$ is present. If ${\beta}_{ij}\equiv {\beta}_{ji}>0$, then ${x}_{i}$ and ${x}_{j}$ are technically complementary. The marginal product of ${x}_{i}$ increases as ${x}_{j}$ increases. If ${\beta}_{ij}\equiv {\beta}_{ji}<0$, then ${x}_{i}$ and ${x}_{j}$ are technically substitutes. Increasing ${x}_{i}$ reduces the marginal productivity of ${x}_{j}$. Table 2 and Table 3 present the definition and summary of the statistics, respectively, for the regression variables.

## 3. Results and Discussion

#### 3.1. Marginal Physical Product and Output Elasticity

_{N}) fertilizer application was positive and the output elasticity value was less than one; both results were significant at the 1% level. The additional N fertilizer use had a significant positive influence on the yield in most plots in the sample. Henceforth, in this section, the term “N” refers to “N fertilizer applied” and “N fertilizer.” Similar interpretations are used for “P” and “K.” Nitrogen fertilizer application increases the height of leaves [41,42], the number of tillers/m

^{2}[41,43,44], and both the number and size of grain [45,46,47].

_{N}is decreasing in Aduthurai (India) (Figure 2), Sukamandi (Indonesia) (Figure 3), Nueva Ecija (Philippines) (Figure 4), and Ha Noi (Vietnam) (Figure 5), but increasing in Suphan Buri (Thailand) at all N rates (Figure 6). The increasing MPP

_{N}suggests a deficiency in N on most plots in the sample areas. Hence, use of an additional fertilizer could exert a positive influence on the yield. The maximum yield will be achieved at N rate where the MPP

_{N}= 0. These rates are 139 kg per ha in Aduthurai, 135 kg per ha in Sukamandi, 160 kg per ha in Nueva Ecija, and 100 kg per ha in Ha Noi. In Aduthurai, applying 139 kg per ha will result in almost 6 tons per ha of grain yield, given all the other factors are constant at the mean level. If more than 139 kg per ha is applied, MPP

_{N}will be negative. This is because excessive N promotes lodging and plants become more attractive to insects and diseases.

_{P}) was positive but the output elasticity is greater than one in Uttar Pradesh (India) (Table 7). The MPP

_{P}was positive and output elasticity was less than one in Sukamandi (Table 8), Can Tho (Vietnam), and Ha Noi (Table 9) at 1% significance level. The magnitude of the estimated coefficients of P reveals the significance of this nutrient in rice production, specifically in Vietnam. For example, a kilogram increase in P increases the yield by 136 kg per ha in Ha Noi.

_{P}and output elasticity at the mean level were negative in Nueva Ecija and Suphan Buri (Table 8), with both being statistically significant at 1% level. There is a possibility that most of the rice straw was retained in the field, and hence those soils were often saturated with P due to continuous P fertilizer application. The extractable Olsen-P level was relatively high for all farms in the sample areas [4]. No additional amount of P fertilizer is required to replenish the P removed with grain and straw. The additional P fertilizer application might result in overapplication. The overapplication of P fertilizer does not necessarily lead to environmental damage, but the ability of the soil to retain P is limited.

_{K}) varied across sites. The MPP

_{K}was positive and output elasticity was less than one at the mean level in Suphan Buri (Table 8). Potassium plays a key role in many metabolic processes in the plant. Meanwhile, a negative MPP

_{K}was observed in Can Tho (Table 9). With the current SSNM fertilizer algorithm, the doses of mineral fertilizers, including N-P-K fertilizers, are determined based on the target yield and the required nutritional needs of the plants. The K requirement of rice is sometimes supplied from plant residues that have been turned under and from K in irrigation water [48]. The SSNM approach takes into account the amount of K recycled from straw yield and the straw management level in the previous season when calculating K fertilizer requirements to avoid excessive K fertilizer use. However, it does not consider the abundance of digestible nutrients in the soil. The water from the Mekong River Delta has high contents of sediments that provide nutrients for crop. Additional K fertilizer would not be beneficial here and could result in overfertilization or negative MPP

_{K}. When fertilizer is overapplied, this may result in the formation of an excess of soluble fertilizer components in the soil and their increased leaching. This would burden the natural environment with a given nutrient, and at the same time reduce the effectiveness of the component application.

#### 3.2. Evidence of Complementarity Among N-P-K Fertilizers

_{N}is higher in absolute value than the direct effect of K on yield. Potassium must not be applied alone, but rather in combination with N. Given this, selective application of fertilizer, i.e., only applying N or K when farmers are faced with cash constraints, might cause more harm than good to the crop. The Wald test failed to reject the null hypothesis that there is no interaction between N and K fertilizers in Aduthurai, Thanjavur, Sukamandi, Nueva Ecija, Can Tho, and Ha Noi.

#### 3.3. Is Yield Response to N Fertilizer Dependent on the Ex Ante State of Soil?

_{N}was invariant to a C content level of approximately 13g/kg, at which point the MVP

_{N}increased up to a C content level of approximately 21 g/kg, after which the MVP

_{N}flattened out, with no further statistically significant growth in rice yield response to N fertilizer beyond that soil fertility level. On the other hand, the MVP

_{N}is rapidly increasing in all Philippine sample plots (Figure 11). The figure also suggests that a farmer with 15 g/kg of C content would get about PHP 84 (approximately USD 2) more profit than a farmer with 5 g/kg of C content, given that they apply the same level of N fertilizer at the mean level. The maximum yield will be achieved at the N rate where the MPP

_{OrgCN}= 0. The $M{R}_{N}$ in Ha Noi did not vary up to a C content level of approximately 17 g/kg, then it increased at an increasing rate up to a C content level of approximately 22 g/kg, after which it increased at a decreasing rate (Figure 12). If further investments are devoted to increasing the soil C content in Vietnam, N fertilizer application is expected to be profitable.

#### 3.4. Non-Nested Hypothesis Test Results

## 4. Conclusions and Policy Implications

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Yang, W.H. Using leaf color charts to estimate leaf nitrogen status of rice. Agronomy
**2003**, 95, 212–217. [Google Scholar] - Chuan, L.; He, P.; Pampolino, M.F.; Johnston, A.M.; Jin, J.; Xu, X.; Zhao, S.; Qiu, S.; Zhou, W. Establishing a scientific basis for fertilizer recommendations for wheat in China: Yield response and agronomic efficiency. Field Crops Res.
**2013**, 140, 1–8. [Google Scholar] [CrossRef] - Dobermann, A.; Witt, C.; Dawe, D. (Eds.) Increasing Productivity of Intensive Rice Systems through Site-Specific Nutrient Management; Science Publishers, Inc.: Enfield, NH, USA; International Rice Research Institute (IRRI): Los Baños, Philippines, 2004; p. 410. [Google Scholar]
- IRRI. International Rice Research Institute. 2020. Available online: http://www.irri.org/about/about.asp (accessed on 10 June 2020).
- Buresh, R.J.; Castillo, R.L.; Dela Torre, J.C.; Laureles, E.V.; Samson, M.I.; Sinohin, P.J.; Guerra, M. Site-specific nutrient management for rice in the Philippines: Calculation of field-specific fertilizer requirements by Rice Crop Manager. Field Crops Res.
**2019**, 239, 56–70. [Google Scholar] [CrossRef] - Lory, J.A.; Scharf, P.C. Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agron. J.
**2003**, 95, 994–999. [Google Scholar] [CrossRef] - Vanotti, M.B.; Bundy, L.G. An alternative rationale for corn nitrogen fertilizer recommendations. J. Prod. Agric.
**1994**, 7, 243–248. [Google Scholar] [CrossRef] - Rodriguez, D.G.P.; Bullock, D.S.; Boerngen, M.A. The origins, implications, and consequences of yield-based nitrogen fertilizer management. Agron. J.
**2019**, 111. [Google Scholar] [CrossRef][Green Version] - Paris, Q. The von Liebig hypothesis. Am. J. Agric. Econ.
**1992**, 74, 1019–1028. [Google Scholar] [CrossRef] - Balasubramanian, V. Farmer adoption of improved nitrogen management technologies in rice farming: Technical constraints and opportunities for improvement. Nutr. Cycl. Agroecosyst.
**1998**, 53, 93–101. [Google Scholar] [CrossRef] - Ishii, S.; Ikeda, S.; Minamisawa, K.; Senoo, K. Nitrogen cycling in rice paddy environments: Past achievements and future challenges. Microbes Environ.
**2011**, 26. [Google Scholar] [CrossRef][Green Version] - Grimm, S.S.; Paris, Q.; Williams, W.A. A von Liebig model for water and nitrogen crop response. West. J. Agric. Econ.
**1987**, 12, 182–192. [Google Scholar] - Janssen, B.H.; Guiking, F.C.T.; van der Eijk, D.; Smaling, E.M.A.; Wolf, J.; van Reuler, H. A system for quantitative evaluation of the fertility of tropical soils (QUEFTS). Geoderma
**1990**, 46. [Google Scholar] [CrossRef][Green Version] - Witt, C.; Dobermann, A.; Abdulrachman, S.; Gines, H.C.; Guanghuo, W.; Nagarajan, R.; Satawatananont, S.; Thuc Son, T.; Sy Tan, P.; Van Tiem, L.; et al. Internal nutrient efficiencies of irrigated lowland rice in tropical and subtropical Asia. Field Crops Res.
**1999**, 63. [Google Scholar] [CrossRef] - Buresh, R.; Pampolino, M.; Witt, C. Field-specific potassium and phosphorus balances and fertilizer requirements for irrigated rice-based cropping systems. Plant Soil
**2010**, 335, 35–64. [Google Scholar] [CrossRef] - Silberberg, E. The Structure of Economics: A Mathematical Analysis; McGraw-Hill Book Company: New York, NY, USA, 1978. [Google Scholar]
- Dawe, D. Reenergizing the Green Revolution in Rice. Am. J. Agric. Econ.
**1998**, 80, 948–953. [Google Scholar] [CrossRef] - Manlay, R.J.; Feller, C.; Swift, M.J. Historical evolution of soil organic matter concepts and their relationships with the fertility and sustainability of cropping systems. Agric. Ecosyst. Environ.
**2007**, 119, 217–233. [Google Scholar] [CrossRef] - Cassman, K.; Dobermann, A.; Cruz, P. Soil organic matter and the indigenous nitrogen supply of intensive irrigated rice systems in the tropics. Plant Soil
**1996**, 267–278. [Google Scholar] [CrossRef] - Tiessen, H.; Cuevas, E.; Chacon, P. The Role of Soil Organic Matter in Sustaining 785 Soil Fertility. Nature
**1994**, 371, 783–785. [Google Scholar] [CrossRef] - Marenya, P.P.; Barrett, C.B. State-conditional fertilizer yield response on Western Kenyan Farms. Am. J. Agric. Econ.
**2009**, 91, 991–1006. [Google Scholar] [CrossRef] - Herrick, J.E.; Wander, M.M. Relationships between soil organic carbon and soil quality in cropped and rangeland soils: The importance of distribution, composition, and soil biological activity. In Soil Processes and the Carbon Cycle; CRC Press: Boca Raton, FL, USA, 2018. [Google Scholar]
- Espe, M.B.; Kirk, E.; van Kessel, C.; Horwath, W.H.; Linquist, B.A. Indigenous Nitrogen Supply of Rice Is Predicted by Soil Organic Carbon. Soil Sci. Soc. Am. J.
**2015**, 79. [Google Scholar] [CrossRef] - Majumder, B.; Mandal, B.; Bandyopadhyay, P.K.; Gangopadhyay, A.; Mani, P.K.; Kundu, A.L.; Mazumdar, D. Organic Amendments Influence Soil Organic Carbon Pools and Rice-Wheat Productivity. Soil Sci. Soc. Am. J.
**2008**, 72. [Google Scholar] [CrossRef] - Reid, D.K. Comment on “The Myth of Nitrogen Fertilization for Soil Carbon Sequestration”, by S.A. Khan et al. in the Journal of Environmental Quality 36:1821–1832. J. Environ. Qual.
**2008**, 37. [Google Scholar] [CrossRef][Green Version] - Manna, M.C.; Swarup, A.; Wanjari, R.H.; Singh, Y.V.; Ghosh, P.K.; Singh, K.N.; Tripathi, A.K.; Saha, M.N. Soil Organic Matter in a West Bengal Inceptisol after 30 Years of Multiple Cropping and Fertilization. Soil Sci. Soc. Am. J.
**2006**, 70, 121. [Google Scholar] [CrossRef] - Khan, S.A.; Mulvaney, R.L.; Ellsworth, T.R.; Boast, C.W. The Myth of Nitrogen Fertilization for Soil Carbon Sequestration. J. Environ. Qual.
**2007**, 36. [Google Scholar] [CrossRef] [PubMed][Green Version] - Li, J.T.; Zhang, B. Paddy Soil Stability and Mechanical Properties as Affected by Long-Term Application of Chemical Fertilizer and Animal Manure in Subtropical China. Pedosphere
**2007**, 17. [Google Scholar] [CrossRef] - López-Bellido, R.J.; Fontán, J.M.; López-Bellido, F.J.; López-Bellido, L. Carbon sequestration by tillage, rotation, and nitrogen fertilization in a mediterranean vertisol. Agron. J.
**2010**, 102. [Google Scholar] [CrossRef] - Luo, L.; Lin, H.; Schmidt, J. Quantitative Relationships between Soil Macropore Characteristics and Preferential Flow and Transport. Soil Sci. Soc. Am. J.
**2010**, 74. [Google Scholar] [CrossRef] - Cleveland, W.S. Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc.
**1979**, 74. [Google Scholar] [CrossRef] - Morris, M.; Kelly, V.A.; Kopicki, R.J.; Byerlee, D. Fertilizer Use in African Agriculture: Lessons Learned and Good Practices; World Bank: Washington, DC, USA, 2007. [Google Scholar]
- Doberman, A. Site-specific nutrient management for intensive rice cropping systems in Asia. Field Crops Res.
**2002**, 74, 37–66. [Google Scholar] [CrossRef] - Walkley, A. A critical examination of a rapid method for determining organic carbon in soils: Effect of variations in digestion conditions and of inorganic soil constituents. Soil Sci.
**1947**, 63, 251–263. [Google Scholar] [CrossRef] - Chambers, R.G. Applied Production Analysis: A Dual Approach; Cambridge University Press: Cambridge, UK, 1988. [Google Scholar]
- Berck, P.; Helfand, G. Reconciling the von Liebig and Differentiable Crop Production Functions. Am. J. Agric. Econ.
**1990**, 72, 985–996. [Google Scholar] [CrossRef] - Berck, P.; Geoghegan, J.; Stohs, S. A strong test of the von Liebig hypothesis. Am. J. Agric.
**2000**, 82, 948–955. [Google Scholar] [CrossRef] - Davidson, R.; MacKinnon, J.G. Testing the specification of multivariate models in the presence of alternative hypotheses. J. Econom.
**1983**, 23. [Google Scholar] [CrossRef] - Ponnamperuma, F.N. The Chemistry of Submerged Soils. Adv. Agron.
**1972**, 24. [Google Scholar] [CrossRef] - Becker, M.; Asch, F. Iron toxicity in rice—Conditions and management concepts. J. Plant Nutr. Soil Sci.
**2005**, 168, 558–573. [Google Scholar] [CrossRef] - Chaturvedi, I. Effect of nitrogen fertilizers on growth, yield and quality of hybrid rice (Oryza sativa). J. Cent. Eur. Agric.
**2006**, 6, 611–618. [Google Scholar] - Mandal, N.N.; Chaudhry, P.P.; Sinha, D. Nitrogen, phosphorus and potash uptake of wheat (var. Sonalika). Environ. Ecol.
**1993**, 10, 297. [Google Scholar] - Rajput, M.K.K.; Ansari, A.H.; Mehdi, S.; Hussain, A.M. Effect of N and P fertilizers alone and in combination with OM on the growth and yield of Toria. Sarhad J. Agri. Res.
**1988**, 4, 3–6. [Google Scholar] - Yoshida, S. Tropical Climate and Its Influence on Rice; International Rice Research Institute: Los Baños, Philippines, 1978; Volume 20. [Google Scholar]
- Rupp, D.; Hubner, H. Influence of Nitrogen fertilization on the mineral content of apple leaves. Erwerbsobstbau
**1995**, 37, 29–31. [Google Scholar] - Jamieson, P.D.; Martin, R.J.; Francis, G.S. Drought influences on grain yield of barley, wheat and maize. N. Z. J. Crops Hortic. Sci.
**1995**, 23, 55–66. [Google Scholar] [CrossRef] - Fisher, R.A.; Aguilar, I.; Laing, D.R. Post-anthesis sink size in a high-yielding dwarf wheat: Yield response to grain number. Aust. J. Agric. Res.
**1977**, 28, 165–175. [Google Scholar] [CrossRef] - De Datta, S.K. Principles and Practice of Rice Production; John Wiley and Sons: Singapore, 1981; pp. 348–419. [Google Scholar]
- Sheriff, G. Efficient Waste? Why Farmers over-Apply Nutrients and the Implications for Policy Design. Appl. Econ. Perspect. Policy
**2005**, 27, 542–557. [Google Scholar] [CrossRef] - Mae, T.; Inaba, A.; Kaneta, Y.; Masaki, S.; Sasaki, M.; Aizawa, M.; Okawa, S.; Hasegawa, S.; Makino, A. A large-grain rice cultivar, Akita-63, exhibits high yields with high physiological N-use efficiency. Field Crops Res.
**2006**, 97, 227–237. [Google Scholar] [CrossRef] - Matsushima, S. Researches on the requirements for achieving high yields in rice. In Science of the Rice Plant: Physiology; Matsuo, T., Ishii, R., Ishihara, K., Hirata, H., Eds.; Food and Agriculture Policy Research Center: Nobunkyo, Tokyo, 1993; Volume 2, pp. 737–747. [Google Scholar]
- Makino, A. Photosynthesis, Grain Yield, and Nitrogen Utilization in Rice and Wheat. Plant Physiol.
**2011**, 155, 125–129. [Google Scholar] [CrossRef] [PubMed][Green Version]

**Figure 3.**Marginal physical product of N (MPP

_{N}) at the mean level in Sukamandi, West Java, Indonesia.

**Figure 10.**Marginal value product of N (MVP

_{N}) based on the plot’s carbon content in Sukamand, Indonesia.

**Figure 11.**Marginal value product of N (MVP

_{N}) based on the plot’s carbon content in Nueva Ecija, Philippines.

**Figure 12.**Marginal value product of N (MVP

_{N}) based on the plot’s carbon content in Ha Noi, Vietnam.

**Figure 13.**Marginal value product of N (MVP

_{N}) based on the plot’s carbon content in Aduthurai, India.

Country | Region/Province | Rice Domain | NO. of Farmers | Cropping System | Climate | Years Included | Cropping Season ^{a} |
---|---|---|---|---|---|---|---|

India | Tamil Nadu | Aduthurai | 40 | Rice-rice | Tropical | 1997 | KR, TH |

Thanjavur | 19 | Rice-rice | Tropical | 1997,1999 | KR, TH | ||

Uttar Pradesh | Pantnagar | 23 | Rice-wheat | Sub-Tropical | 1997 | KH | |

Indonesia | West Java | Sukamandi | 30 | Rice-rice | Tropical | 1996,1998 | DS, WS |

Philippines | Nueva Ecija | Maligaya | 50 | Rice-rice | Tropical | 1995–1996 | DS, WS |

Thailand | Central Plain | Suphan Buri | 27 | Rice-rice | Tropical | 1995–1996 | DS, WS |

Vietnam | Mekong Delta | Can Tho | 32 | Rice-rice-rice | Tropical | 1996 | DS, WS |

Red River Delta | Hanoi | 24 | Rice-rice-maize | Sub-Tropical | 1997 | ER, LR |

^{a}High yielding season: KR—Kuruvai, DS—Dry Season, ER—Early Rice; Low yielding season: TH—Thaladi, WS—Wet Season, LR—Late Rice.

Variable | Description |
---|---|

Rice output (kg/ha) | Dependent variable (Y). |

Kilograms of rice harvested per hectare per season in a given year | |

Nitrogen applied (N/ha) | Kilogram of N per ha from fertilizers applied |

Phosphorus applied (P/ha) | Kilogram of P per ha from fertilizers applied |

Potassium (K/ha) | Kilogram of K per ha from fertilizers applied |

Org C | Amount of carbon content in the soil (g/kg) |

Age (year) | Age in years of the person responsible for production decisions on the plot |

Educ (year) | Total years of schooling completed by the farmer |

Farm area (ha) | Size of farm owned by the farmer |

High yielding season (HYS) | Dummy variable. |

HYS = 1; high yielding season | |

HYS = 0; low yielding season |

Site/Variable | No. of Observations | Mean | Standard Deviation |
---|---|---|---|

INDIA | |||

Aduthurai | |||

Rice output (kg/ha) | 1121 | 5128.03 | 1454.71 |

N applied (kg/ha) | 1121 | 52.87 | 64.9 |

P applied (kg/ha) | 1121 | 17.54 | 14.41 |

K applied (kg/ha) | 1121 | 32.95 | 30.87 |

Org C (g/kg) | 1121 | 9.04 | 1.25 |

Age (year) | 867 | 47.31 | 11.74 |

Educ (year) | 274 | 10.58 | 2.84 |

Farm area (ha) | 1121 | 0.3 | 0.08 |

HYS | 1121 | 0.37 | 0.48 |

Thanjavur | |||

Rice output (kg/ha) | 77 | 4632.96 | 1281.16 |

N applied (kg/ha) | 77 | 48.34 | 56.06 |

P applied (kg/ha) | 77 | 10.6 | 15.31 |

K applied (kg/ha) | 77 | 20.53 | 30.05 |

Org C (g/kg) | 77 | 71.15 | 7.88 |

Age (year) | - | - | - |

Educ (year) | - | - | - |

Farm area (ha) | 75 | 0.31 | 0.17 |

HYS | 77 | 0.92 | 0.27 |

Uttar Pradesh | |||

Rice output (kg/ha) | 84 | 5068.41 | 1190.91 |

N applied (kg/ha) | 84 | 62.97 | 72.61 |

P applied (kg/ha) | 84 | 24.64 | 8.44 |

K applied (kg/ha) | 84 | 30.05 | 21 |

Org C (g/kg) | 84 | 11.89 | 2.71 |

Age (year) | 80 | 50.35 | 11.6 |

Educ (year) | 40 | 11.1 | 3.37 |

Farm area (ha) | 84 | 0.36 | 0.08 |

HYS | 84 | 0 | 0 |

INDONESIA | |||

Sukamandi, West Java | |||

Rice output (kg/ha) | 480 | 4046.43 | 1372.89 |

N applied (kg/ha) | 480 | 55.36 | 66.03 |

P applied (kg/ha) | 480 | 11.24 | 12.77 |

K applied (kg/ha) | 480 | 17.37 | 23.83 |

Org C (g/kg) | 480 | 15.7 | 4.97 |

Age (year) | 435 | 43.3 | 13.81 |

Educ (year) | 142 | 6.92 | 3.28 |

Farm area (ha) | 480 | 0.99 | 1.18 |

HYS | 480 | 0.78 | 0.42 |

PHILIPPINES | |||

Nueva Ecija, Philippines | |||

Rice output (kg/ha) | 630 | 4760.10 | 1559.10 |

N applied (kg/ha) | 630 | 41.96 | 63.98 |

P applied (kg/ha) | 630 | 13.79 | 12.89 |

K applied (kg/ha) | 630 | 22.83 | 22.11 |

Org C (g/kg) | 630 | 10.39 | 2.78 |

Age (year) | 558 | 51.02 | 13.6 |

Educ (year) | 179 | 7.32 | 4.03 |

Farm area (ha) | 630 | 1.73 | 0.96 |

HYS | 630 | 1 | 0 |

THAILAND | |||

Suphan Buri, Thailand | |||

Rice output (kg/ha) | 660 | 3572.47 | 960.24 |

N applied (kg/ha) | 660 | 34.61 | 52.66 |

P applied (kg/ha) | 660 | 17.13 | 13.99 |

K applied (kg/ha) | 660 | 16.69 | 23.52 |

Org C (g/kg) | 660 | 10.49 | 6.67 |

Age (year) | 651 | 46.91 | 8.84 |

Educ (year) | 216 | 4.78 | 1.85 |

Farm area (ha) | 660 | 1.55 | 0.96 |

HYS | 660 | 0.65 | 0.48 |

VIETNAM | |||

Can Tho, Vietnam | |||

Rice output (kg/ha) | 591 | 3894.34 | 1415.28 |

N applied (kg/ha) | 591 | 32.22 | 54.18 |

P applied (kg/ha) | 591 | 15.38 | 13.82 |

K applied (kg/ha) | 591 | 19.2 | 22.38 |

Org C (g/kg) | 591 | 18.54 | 4.11 |

Age (year) | 591 | 47.8 | 11 |

Educ (year) | 591 | 6.86 | 3.65 |

Farm area (ha) | 591 | 0.81 | 0.67 |

HYS | 591 | 0.65 | 0.48 |

Ha Noi, Vietnam | |||

Rice output (kg/ha) | 96 | 5627.50 | 1389.42 |

N applied (kg/ha) | 96 | 48.12 | 50.67 |

P applied (kg/ha) | 96 | 24.25 | 8.1 |

K applied (kg/ha) | 96 | 51.05 | 14.71 |

Org C (g/kg) | 96 | 14.74 | 4.98 |

Age (year) | 48 | 47.75 | 9.15 |

Educ (year) | 24 | 7.08 | 2.65 |

Farm area (ha) | 96 | 0.08 | 0.02 |

HYS | 96 | 0.96 | 0.2 |

Variable | Aduthurai | Uttar Pradesh | Thankjavur | |||
---|---|---|---|---|---|---|

Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |

Nitrogen (N) | 25.59 *** | 25.30 *** | −128.7 *** | −107.2 *** | −9.845 | −12.7 |

−5.618 | −5.634 | −36.95 | −35.31 | −29.23 | −30.46 | |

N-squared | −0.107 *** | −0.107 *** | 0.378 ** | 0.366 ** | −0.00907 | 0.0372 |

−0.0131 | −0.0131 | −0.156 | −0.15 | −0.108 | −0.121 | |

Phosphorus (P) | −19.97 | −17.67 | 1024.5 *** | 855.8** | 24.54 | 84.46 |

−15.7 | −15.78 | −345.6 | −328.7 | −99.92 | −120.9 | |

P-squared | 0.956 *** | 0.870 ** | −20.86 | −14.18 | 0.0359 | 0.353 |

−0.362 | −0.365 | −14.61 | −13.94 | −0.653 | −0.724 | |

Potassium (K) | 5.74 | 6.374 | −1054.7 ** | −840.8 ** | 53.90 ** | 34.73 |

−7.139 | −7.175 | −426.7 | −406.5 | −25.57 | −32.41 | |

K-squared | −0.114 *** | −0.111 *** | 2.721 | 2.866 | −0.122 | −0.0315 |

−0.0392 | −0.0392 | −2.452 | −2.308 | −0.159 | −0.177 | |

N × P | −0.221 * | −0.214 * | −1.34 | −1.914 | −0.0488 | −0.702 |

−0.118 | −0.118 | −2.173 | −2.085 | −1.18 | −1.381 | |

P × K | −0.0336 | −0.0489 | 19.93 | 12.39 | −1.109 | −1.474 * |

−0.149 | −0.149 | −17.24 | −16.41 | −0.735 | −0.84 | |

N × K | 0.0761 | 0.0727 | 4.566 *** | 4.102 *** | −0.141 | 0.0711 |

−0.0496 | −0.05 | −1.208 | −1.147 | −0.264 | −0.349 | |

Organic Carbon (OrgC) | 173.5 | 175.9 | −149.3 | −286.7 | 882.2 *** | 785.4 *** |

−217.4 | −218.4 | −200.5 | −209.5 | −233.3 | −261.8 | |

OrgC-squared | −7.558 | −8.079 | 5.506 | 10.39 | −5.941 *** | −5.296 *** |

−11.61 | −11.67 | −8.891 | −9.667 | −1.623 | −1.809 | |

OrgC × N | 0.618 | 0.629 | 0.537 | 0.485 | 0.283 | 0.24 |

−0.463 | −0.463 | −0.633 | −0.6 | −0.291 | −0.319 | |

High Yielding Season (HYS) | 138.1 * | - | 326.2 | |||

−71.63 | - | −588.4 | ||||

Farm area | 2250.2 | −21,061.1 ** | 448.5 | |||

−2518.3 | −8018.8 | −2741.9 | ||||

Farm area × farm area | −2860.5 | 40,513.2 *** | 359.8 | |||

−4017.7 | −13,753.9 | −2441.7 | ||||

Constant | 3310.8 *** | 2879.2 ** | 9308.7 *** | 11,624.0 *** | 2858.2 *** | 2550.6 *** |

−1013.8 | −1120.1 | −1756.9 | −1900.3 | −8317.3 | −9128.2 | |

No. of observations | 1121 | 1121 | 84 | 84 | 77 | 75 |

Adjusted R-squared | 0.408 | 0.409 | 0.51 | 0.567 | 0.642 | 0.629 |

Akaike Info Criteria | 18,934.8 | 18,935.5 | 1380.163 | 1371.333 | 1253.152 | 1226.733 |

Bayesian Info Criteria | 19,000.09 | 19,015.85 | 1411.763 | 1407.795 | 1283.622 | 1263.812 |

**Table 5.**Production function estimates using two versions of the quadratic model in Indonesia, Philippines, and Thailand.

Variable | West Java, Indonesia | Nueva Ecija, Phillippines | Suphan Buri, Thailand | |||
---|---|---|---|---|---|---|

Model 1 | Model 2 | Model 1 | Model 2 | Model 1 | Model 2 | |

N | 21.32 *** | 24.11 *** | 17.43 *** | 19.05 *** | −7.731 | −4.565 |

−3.732 | −3.209 | −6.287 | −6.297 | −5.871 | −5.694 | |

N-squared | −0.0881 *** | −0.0822 *** | −0.0662 *** | −0.0701 *** | 0.0864 ** | 0.0609 |

−0.0141 | −0.012 | −0.0187 | −0.0187 | −0.042 | −0.0412 | |

P | −10.75 | 8.973 | 22.36 | 25.85 | 78.79 *** | 74.12 *** |

−24.04 | −20.54 | −59.25 | −59.16 | −24.87 | −24.06 | |

P-squared | 0.448 | −0.474 | −1.052 | −1.19 | −0.476 | −0.446 |

−0.759 | −0.65 | −2.562 | −2.554 | −0.476 | −0.464 | |

K | −40.64 *** | −19.55 | −9.655 | −13.03 | - | −14.42 |

−14.04 | −12.06 | −29.42 | −29.77 | - | −13.62 | |

K-squared | 0.395 *** | 0.198 * | 0.841 * | 0.851 * | 7.279 | 7.653 * |

−0.127 | −0.109 | −0.456 | −0.454 | −4.674 | −4.559 | |

N × P | 0.255 * | 0.164 | 0.0975 | 0.0724 | −0.381 * | −0.334 |

−0.13 | −0.112 | −0.332 | −0.332 | −0.229 | −0.225 | |

P × K | 0.844 ** | 0.493 * | −0.837 | −0.727 | −13.41 * | −13.96 * |

−0.327 | −0.281 | −1.405 | −1.407 | −7.782 | −7.584 | |

N × K | 0.0532 | 0.0229 | −0.154 | −0.159 | 1.787 ** | 1.748 ** |

−0.0876 | −0.0748 | −0.19 | −0.191 | −0.893 | −0.867 | |

OrgC | 91.64 | 73.59 | 74.54 | 137.9 | −17.47 | 29.82 |

−72.39 | −63.72 | −124.2 | −126.3 | −19.83 | −28.79 | |

OrgC-squared | 2.32 | 1.48 | −3.101 | −5.42 | −0.0145 | −1.71 |

−2.208 | −1.927 | −5.713 | −5.765 | −0.983 | −1.222 | |

OrgC × N | −0.102 | −0.273 ** | 0.684 ** | 0.631 * | 0.0806 | 0.0412 |

−0.157 | −0.136 | −0.339 | −0.339 | −0.0952 | −0.0921 | |

HYS | 1402.6 *** | - | 533.7 *** | |||

−109.3 | - | −127.8 | ||||

Farm area | 620.2 *** | −474.5 ** | −8.541 | |||

−129 | −187.5 | −202.3 | ||||

Farm area × farm area | −122.3 *** | 103.1 ** | −0.913 | |||

−24.54 | −43.77 | −48.16 | ||||

Constant | 1338.7 ** | 449.1 | 3719.0 *** | 3744.8 *** | 3471.4 *** | 2901.3 *** |

−562.7 | −494.8 | −660.1 | −659.9 | −96.53 | −355.6 | |

No. of observations | 480 | 480 | 630 | 630 | 660 | 660 |

Adjusted R-squared | 0.469 | 0.615 | 0.287 | 0.292 | 0.2 | 0.256 |

Akaike Info Criteria | 8006.938 | 7855.506 | 10,851.17 | 10,848.61 | 10,802.13 | 10,757.67 |

Bayesian Info Criteria | 8061.198 | 7922.286 | 10,908.97 | 10,915.29 | 10,856.04 | 10,825.05 |

Variable | Can Tho | Ha Noi | ||
---|---|---|---|---|

Model 1 | Model 2 | Model 1 | Model 2 | |

N-squared | −8.633 | 16.94 ** | 83.23 | 69.25 |

−14.6 | −8.549 | −65.47 | −63.38 | |

Nsq | 0.064 | 0.0341 | −0.221 | −0.256 * |

−0.0948 | −0.0557 | −0.143 | −0.139 | |

P-squared | 235.8 *** | 57.89 | 231.9 | 204.3 |

−83.93 | −49.16 | −165.8 | −164.4 | |

Psq | −0.525 | 2.128 ** | 1.464 | 2.622 |

−1.514 | −0.878 | −2.66 | −2.611 | |

K-squared | −38.39 | −17.87 | −53.79 | −139.4 |

−81.27 | −47.03 | −140.9 | −139.4 | |

Ksq | 0.256 | −1.329 | 0.376 | 0.743 |

−1.673 | −0.967 | −0.577 | −0.579 | |

N × P | −1.147 | −1.209 ** | −2.550 ** | −2.741 *** |

−0.842 | −0.491 | −0.996 | −0.997 | |

P × K | −3.409 | 0.581 | −0.401 | −0.178 |

−3.233 | −1.878 | −1.505 | −1.527 | |

N × K | 0.861 | 0.233 | 0.175 | 0.639 |

−0.538 | −0.31 | −0.879 | −0.872 | |

OrgC | −521.1 *** | −446.2 *** | −205.1 | −265.4 |

−109.3 | −62.95 | −156.5 | −160.2 | |

OrgC-squared | 12.61 *** | 9.917 *** | 11.01 ** | 12.61 ** |

−2.761 | −1.592 | −4.798 | −4.834 | |

OrgC × N | −0.205 | −0.197 | 1.168 ** | 1.224 ** |

−0.277 | −0.159 | −0.526 | −0.514 | |

HYS | 2232.2 *** | 1122.1 * | ||

−68.55 | −568.4 | |||

Farm area | 507.6 *** | −73,770.9 ** | ||

−141.2 | −35,356.7 | |||

Farm area × farm area | −103.3 ** | 379,887.0 ** | ||

−45.94 | −178,261.7 | |||

Constant | 8650.2 *** | 6384.1 *** | 341.9 | 5167.6 |

−1052.8 | −616.5 | −7097.7 | −7221.5 | |

No. of observations | 591 | 591 | 96 | 96 |

Adjusted R-squared | 0.142 | 0.718 | 0.498 | 0.536 |

Akaike Info Criteria | 10,175.29 | 9520.387 | 1607.71 | 1602.749 |

Bayesian Info Criteria | 10,232.26 | 9590.496 | 1641.047 | 1643.778 |

Variable | Aduthurai | Thanjavur | Uttar Pradesh | |||
---|---|---|---|---|---|---|

MPP | Output Elasticity | MPP | Output Elasticity | MPP | Output Elasticity | |

Total N (kg) | 18.34 (1.36) *** | 0.19 (0.02) *** | 2.01 (12.85) | 0.02 (0.13) | 20.77 (22.67) | −0.09 (0.17) |

Total P (kg) | −0.04 (6.59) | −0.01 (0.02) | 27.77 (58.08) | 0.06 (0.13) | 408.94 (196.77) ** | 1.20 (0.87) ** |

Total K (Kg) | 2.05 (3.97) | 0.02 (0.02) | 21.25 (16.9) | 0.09 (0.07) | −104.99 (80.55) | −1.17 (0.49) ** |

Org C (g/kg) | 63.06 (30.72) ** | 0.12 (0.05) ** | 43.32 (13.75) *** | 0.66 (0.21) *** | −9.16 (47.81) | −0.02 (0.13) |

Farm area | 533.90 (472.02) | 0.02 (0.03) | 685.97 (1467.44) | 0.04 (0.10) | 918.68 (302.20) *** | 0.77 (0.25) *** |

**Table 8.**Marginal physical product (MPP) and output elasticity at the mean level in Indonesia, Philippines, and Thailand.

Variable | Sukamandi, West Java, Indonesia | Nueva Ecija, Philippines | Suphan Buri, Thailand | |||
---|---|---|---|---|---|---|

MPP | Output Elasticity | MPP | Output Elasticity | MPP | Output Elasticity | |

Total N (kg) | 12.96 (1.04) *** | 0.18 (0.01) *** | 17.10 (2.43) *** | 0.15 (0.02) *** | 36.27 (8.27) *** | 0.32 (0.08) *** |

Total P (kg) | 15.94 (7.68) ** | 0.04 (0.02) ** | −20.53 (21.89) | −0.06 (0.06) | −270.83 (64.42) *** | −0.70 (0.17) *** |

Total K (Kg) | −5.86 (5.06) | −0.02 (0.02) | 9.15 (13.36) | 0.04 (0.06) | 230.31 (54.44) *** | 0.48 (0.12) *** |

Org C (g/kg) | 104.96 (12.02) *** | 0.41 (0.05) *** | 51.82 (20.87) ** | 0.11 (0.05) ** | −3.98 (8.17) | −0.01 (0.02) |

Farm area | 380.40 (94.06) *** | 0.09 (0.02) *** | −123.87 (63.82) * | −0.04 (0.02) * | −11.78 (58.89) | −0.01 (0.03) |

Variable | Can Tho | Ha Noi | ||
---|---|---|---|---|

MPP | Output Elasticity | MPP | Output Elasticity | |

Total N (kg) | 1.36 (0.82) | 0.01 (0.05) | 28.81 (6.30) *** | 0.24 (0.05) *** |

Total P (kg) | 95.54 (24.34) *** | 0.38 (0.10) *** | 190.42 (55.69) *** | 0.82 (0.24) *** |

Total K (Kg) | −52.44 (15.45) *** | −0.26 (0.08) *** | −37.11 (46.42) | −0.33 (0.42) |

Org C (g/kg) | −84.76 (7.59) *** | −0.40 (0.04) *** | 165.18 (32.24) *** | 0.43 (0.08) *** |

Farm area | 340.30 (80.21) *** | 0.07 (0.02) *** | −575.38 (244.84) ** | −0.04 (0.03) * |

Hypothesis: Parameter β_{ij} | Aduthurai, India | Thanjavur, India | Uttar Pradesh, India | Sukamandi, WJ, Indonesia | ||||
---|---|---|---|---|---|---|---|---|

F Value | p-Value | F Value | p-Value | F Value | p-Value | F Value | p-Value | |

NP = 0 | 2.87 | 0.09 | 0.29 | 0.61 | 1.44 | 0.23 | 2.93 | 0.09 |

NP < 0 | 0.95 | 0.69 | 0.82 | 0.07 | ||||

NP > 0 | 0.04 | 0.31 | 0.18 | 0.93 | ||||

PK = 0 | 0.12 | 0.73 | 3.21 | 0.08 | 0.86 | 0.36 | 3.88 | 0.08 |

PK < 0 | 0.64 | 0.95 | 0.23 | 0.04 | ||||

PK > 0 | 0.36 | 0.05 | 0.77 | 0.96 | ||||

NK = 0 | 2.58 | 0.10 | 0.04 | 0.84 | 19.91 | 0.00 | 0.12 | 0.76 |

NK < 0 | 0.05 | 0.42 | 0.00 | 0.38 | ||||

NK > 0 | 0.95 | 0.58 | 0.99 | 0.62 | ||||

OrgCN = 0 | 1.78 | 0.18 | 0.74 | 0.45 | 0.69 | 0.42 | 4.76 | 0.04 |

OrgCN < 0 | 0.09 | 0.23 | 0.21 | 0.98 | ||||

OrgCN > 0 | 0.91 | 0.77 | 0.79 | 0.02 | ||||

Hypothesis: Parameter β_{ij} | Nueva Ecija, Philippines | Suphan Buri, Thailand | Can Tho, Vietnam | Hanoi, Vietnam | ||||

F Value | p-Value | F Value | p-Value | F Value | p-Value | F Value | p-Value | |

NP = 0 | 0.09 | 0.82 | 1.73 | 0.13 | 8.39 | 0.00 | 11.57 | 0.00 |

NP < 0 | 0.41 | 0.93 | 0.99 | 0.99 | ||||

NP > 0 | 0.59 | 0.07 | 0.01 | 0.01 | ||||

PK = 0 | 0.47 | 0.61 | 22.78 | 0.06 | 0.15 | 0.70 | 0.02 | 0.88 |

PK < 0 | 0.70 | 0.97 | 0.35 | 0.55 | ||||

PK > 0 | 0.30 | 0.03 | 0.65 | 0.45 | ||||

NK = 0 | 1.52 | 0.41 | 22.62 | 0.04 | 0.92 | 0.34 | 0.47 | 0.49 |

NK < 0 | 0.79 | 0.02 | 0.17 | 0.25 | ||||

NK > 0 | 0.21 | 0.98 | 0.83 | 0.75 | ||||

OrgCN = 0 | 3.97 | 0.06 | 0.22 | 0.65 | 2.09 | 0.15 | 3.05 | 0.08 |

OrgCN < 0 | 0.03 | 0.33 | 0.93 | 0.04 | ||||

OrgCN > 0 | 0.97 | 0.67 | 0.07 | 0.96 |

Site/ Alternative Hypothesis | Null Hypothesis | |||
---|---|---|---|---|

Linear Von Liebig | Squared | Square-Root | Non-Linear Von Liebig | |

India | ||||

Aduthurai | ||||

Linear von Liebig | - | 12.41 *** | 1.04 | 10.88 ** |

Squared | 1.12 | - | 0.03 | 1.66 |

Square-root | 0.78 | 9.84 *** | - | 1.7 |

Non-linear von Liebig | 21.57 *** | 13.74 *** | 3.81 * | - |

ALL | 3.10 ** | 6.59 *** | 2.91 ** | 0.64 |

Thanjavur | ||||

Linear von Liebig | - | 1.81 | 1.83 | 2.09 |

Squared | 5.11 ** | - | 0.94 | 3.14 * |

Square-root | 4.40 ** | 0 | - | 3.70 * |

Non-linear von Liebig | 69.16 *** | 4.13 ** | 7.48 ** | - |

ALL | 2.44 * | 1.34 | 2.04 | 1.33 |

Uttar Pradesh | ||||

Linear von Liebig | 0.15 | 1.87 | 0.8 | |

Squared | 3.84 * | 0.48 | 11.61 *** | |

Square-root | 3.47 * | 0.12 | 12.50 *** | |

Non-linear von Liebig | 6.94 *** | 0.26 | 0.83 | |

ALL | 1.54 | 0.12 | 1.09 | 4.35 *** |

West Java, Indonesia | ||||

Linear von Liebig | - | 0.1 | 2.46 | 89.54 *** |

Squared | 53.74 *** | - | 2.85* | 268.64 |

Square-root | 58.63 *** | 3.47 * | - | 14.80 *** |

Non-linear von Liebig | 51.36 *** | 0.68 | 0.06 | 260.41 *** |

ALL | 28.56 *** | 1.94 | 2.58 * | 91.48 *** |

Nueva Ecija, Philippines | ||||

Linear von Liebig | 0.05 | 0.69 | 3.22* | |

Squared | 23.17 *** | 2.86* | 0.47 | |

Square-root | 25.66 *** | 3.18 * | 0.7 | |

Non-linear von Liebig | 49.01 *** | 2.01 | 2.49 | |

ALL | 15.97 *** | 7.04 *** | 8.31 *** | 0.67 |

Site/Alternative Hypothesis | Null Hypothesis | |||

Linear von Liebig | Squared | Square-root | Non-linear von Liebig | |

Suphan Buri, Thailand | ||||

Linear von Liebig | 0.01 | 22.57 *** | 66.44 *** | |

Squared | 24.70 *** | 26.04 *** | 17.24 *** | |

Square-root | 28.87 *** | 6.66 ** | 17.81 *** | |

Non-linear von Liebig | 10.21 *** | 0.71 | 21.44 *** | |

ALL | 2.52 * | 18.31 *** | 6.09 *** | |

Vietnam | ||||

Can Tho | ||||

Linear von Liebig | - | 0.22 | 2 | 6.45 * |

Squared | 21.48 *** | - | 7.62 *** | 7.91 *** |

Square-root | 15.49 *** | 0.01 | - | 1.65 |

Non-linear von Liebig | 27.05 *** | 2.04 | 9.87 *** | - |

ALL | 10.31 *** | 1.98 | 6.46 *** | 4.55 *** |

Hanoi | ||||

Linear von Liebig | - | 0.13 | 6.41 ** | 0.16 |

Squared | 6.20 ** | - | 0.08 | 3.8 * |

Square-root | 5.46 ** | 6.3 7 ** | - | 3.30 * |

Non-linear von Liebig | 17.08 *** | 0.67 | 5.21 ** | - |

ALL | 4.88 *** | 1.45 | 2.35 * | 4.08 *** |

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |

© 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Rodriguez, D.G.P.
An Assessment of the Site-Specific Nutrient Management (SSNM) Strategy for Irrigated Rice in Asia. *Agriculture* **2020**, *10*, 559.
https://doi.org/10.3390/agriculture10110559

**AMA Style**

Rodriguez DGP.
An Assessment of the Site-Specific Nutrient Management (SSNM) Strategy for Irrigated Rice in Asia. *Agriculture*. 2020; 10(11):559.
https://doi.org/10.3390/agriculture10110559

**Chicago/Turabian Style**

Rodriguez, Divina Gracia P.
2020. "An Assessment of the Site-Specific Nutrient Management (SSNM) Strategy for Irrigated Rice in Asia" *Agriculture* 10, no. 11: 559.
https://doi.org/10.3390/agriculture10110559